# Artificial Intelligence

## 1 Tmp

• Conditional Random Field (CRF):
• Markov property: a stochastic process has Markov property, if the conditional probabilistic distribution of future states depends only on current state, not the sequence of events preceded it.
• Markov process: a stochastic process with Markov property
• Hidden Markov Model (HMM): Markov process with unobserved states.
• strong Markov property: it indicates ordinary Markov property. It introduces an extra variable time t, and says for each t, some property holds.
• Markov assumption: assume Markov property holds in a given model
• Markov Random Field (MRF): THIS IS JUST THE MARKOV NETWORK. It extend the Markov property to two or more dimensions, or to random variables defined for an interconnected network of items, instead of a sequence, such as the graphical model.
• Ising model: this is an example of Markov Random Field.
• Markov chain: a discrete-time stochastic process satisfying the Markov property

### 1.1TODO PCA & PCR & PLS

In case of high dimensionality, many models would fail, and this is known as curse of dimensionality. For example, when the dimension is high, KNN need not to be close to the target point. In general, any method that attempts to produce local varying functions in small isotropic neighborhoods will run into problems in high dimensions.

#### 1.1.1Principal Component Analysis (PCA)

The following are some criteria for the direction selection, they all talk about the same thing:

• The first principal component direction is that along which the observation vary the most.
• This also yields the highest variance.
• It also defines the line that is as close as possible to the data.
• projected observations are as close as possible to the original observations.

The second principal component $$Z_2$$ is a linear combination of the variables that is uncorrelated with $$Z_1$$, and has largest variance subject to this constraint. Actually $$Z_1$$ and $$Z_2$$ are always orthogonal.

#### 1.1.2Principal Component Regression (PCR)

Construct the first M principal components, and do linear regression on the new predictors.

#### 1.1.3Partial Least Squares (PLS)

The directions identified by PCA is in an unsupervised way, i.e. it does not use response Y.

Set each $$\phi_{j1}$$ equal to the coefficient from teh simple linear regression of Y onto Xj. Intuitively PLS places the highest weight on the variables that are most strongly related to the response.

Second PLS direction is by

1. adjust each of the variables for Z1, by regressing each variable on Z1 and taking residuals This captures the remaining information that has not been explained by the first PLS direction
2. use this orthogonalized data in exactly the same fashion as Z1.
3. Repeat M times.

### 1.3 Gibbs sampling

It is one kind of Monte Carlo Markov Chain method, for sampling from posterior distribution. The key is to sample from variables' conditional distribution: for each variable, sample from the conditional distribution where all other variables are fixed to their previous value.

$x^i_1 \sim p(X_1=x_1 | X_2 = x^{i-1}_2, X_3=.., X_4=.., ...)$

The convergence is when the sample values have the same distribution as their posterior joint distribution. The theory of MCMC guarantees that the stationary distribution of the samples is the target joint posterior distribution.

• How do we know they converge?
• what do you mean by "sample from conditional distribution"?

### 1.4 Explaining away effect

Two causes for an effect, if we can confirm one cause, it will reduce the happen of the other.

However, one cause does not provide any evidence of the other cause, because they are independent (??). But in probability theory, it SHOULD reduce the other cause.

For example, you scored 100/100 out of a test, can have two causes:

• paper is too easy
• you are too smart

Given that the paper is too easy, I tend not to think you are too smart.

### 1.5 Chi-square Independence test

Using the example taken from Wikipedia

A B C D total
White collar 90 60 104 95 349
Blue collar 30 50 51 20 151
No collar 30 40 45 35 150
Total 150 150 200 150 650

We basically use the 3x4 table cells. This is the observed number.

1. We first calculate a table for the expected number, for example for (A, white collar), we calculate it as (150 /650 * 349 = 80.54).
2. Then, we compute the difference in each cell, as the test statistics, for example (90 - 80.54) / 80.54 = 1.11
3. We sum up all these difference values in the table cells
4. calculate the Degree of Freedom (DF) as $$(ncol-1)*(nrow-1)$$
5. Using the DF and the test statistics, we can look up table to see what is the probability of seen such a distribution if the data is independent (our null hypothesis)

## 2 Probabilistic Model

### 2.1 Discrete Variables

We define the outcome space as the space of possible outcomes, denoted as $$\Omega$$. We also define the events $$S$$. Each event $$\alpha \in S$$ is a subset of $$\Omega$$. For example, for a dice throwing, $$\Omega=\{1,2,3,4,5,6\}$$, $$\{1\}$$ and $$\{2,4,6\}$$ are events.

Then, we can formally define a probability distribution P over $$(\Omega, S)$$ as a mapping from events in $$S$$ to real values that satisfies the following conditions:

• $$P(\alpha) \ge 0$$ for all $$\alpha \in S$$
• $$P(\Omega)=1$$
• If $$\alpha,\beta \in S$$ and $$\alpha \cap \beta = \emptyset$$, then $$P(\alpha \cup \beta) = P(\alpha) + P(\beta)$$

This definition also implies several interesting properties:

• $$P(\emptyset) = 0$$
• $$P(\alpha \cup \beta) = P(\alpha) + P(\beta) - P(\alpha \cup \beta)$$

There are two views of probability distribution. The frequentist interpretation views it as the frequencies of events, and the subjective interpretation views it as the degrees of belief.

We define conditional probability distribution (CPD) as $$P(\beta | \alpha) = \frac{P(\alpha \cap \beta)}{P(\alpha)}$$. From this, we can get:

• chain rule: $$P(\alpha_1 \cap ... \cap \alpha_k) = P(\alpha_1) P(\alpha_2 | \alpha_1) ... P(\alpha_k | \alpha_1 \cap ... \cap \alpha_{k-1})$$
• bayes' rule: $$P(\beta|\alpha) = \frac{P(\alpha|\beta)P(\beta)}{P(\alpha)}$$

So far, we use events. The use of events are cumbersome in some case. For example, we need to use GradeA, GradeB, GradeC, etc to represent the events for different grades. Thus, we introduce random variables (RV) as a way of reporting an attribute of the outcome. Formally, a random variable is defined by a function that associates with each outcome in $$\Omega$$ a value. We use $$Val(X)$$ to denote the set of values a random variable $$X$$ can take. We use upper case $$X$$ to denote the random variables, use lower case $$x$$ to denote a generic value of $$X$$, and use $$x^1, ..., x^k$$ to denote the possible values. We use bold typeface X to denote a set of random variables, and lowercase bold typeface x to denote an assignment to all variables in the set. A distribution over random variables with $$k$$ values are called Multinomial Distribution, one over binary random variables are called Bernoulli Distribution.

Once we define the random variables, we can define marginal distribution over X as the distribution over events that can be described using X, denoted by $$P(X)$$. We may also be interested in the distribution over multiple random variables. In general, a joint distribution over a set $$X = \{X_1, ..., X_n\}$$, denoted as $$P(X_1, ..., X_n)$$, is the distribution that assigns probabilities to events that can be described using these random variables. We use $$\xi$$ to refer to a full assignment to the variables in $$X$$. The conditional probability in terms of random variables are intuitive.

### 2.2 Continuous Variables

For continuous random variables, probability density function (PDF) is used for continuous random variables, because the vector is infinite. I record some distributions here:

Uniform distribution over [a,b], denoted as $$X \sim Unif[a,b]$$, if $$p(x) = \frac{1}{b-1}$$ in [a,b] otherwise 0.

Gaussian distribution with mean $$\mu$$ and variance $$\sigma^2$$, denoted as $$X \sim N(\mu; \sigma^2)(x)$$, if

$p(X)=\frac{1}{\sqrt{2\pi} \sigma} e^{-\frac{(x-\mu)^2}{2 \sigma^2}}$

Multivariate normal distribution:

$N(x|\mu, \Sigma) = \frac{1}{(2\pi)^{D/2}} \frac{1}{|\Sigma|^{1/2}} exp\{-\frac{1}{2}(x-\mu)^T \Sigma^-1 (x-\mu)\}$

### 2.3 Expectation, Variance, Entropy

Expectation is $$E_x[f]=\sum_x p(x)f(x)$$. Variance is

\begin{align} var[f] & = E[(f(x) - E[f(x)])^2]\\ & = E[f(x)^2] - E[f(x)]^2 \end{align}

The square root of variance is called standard deviation.

Specially for x, it is

$var[x] = E[x^2] - E[x]^2$

The covariate of two random variables x and y is defined as

\begin{align} cov[x,y] & = E_{x,y}[(x-E[x])(y-E[y])]\\ & = E_{x,y}[xy] - E[x]E[y] \end{align}

The entropy is from information theory. We want to calculate how much information we gain after seeing an observation from a probability distribution $$p(x)$$. Intuitively, if we see a highly improbable event, we received a lot of information. If we observe a very likely event, we gain less information. The extreme case, if we observe an event that is certain to happen, we would receive no information. How can we quantify this?

The straightforward thinking is that, the information gain of two independent observations $$x$$ and $$y$$ should be the sum of the gain of the two, i.e. $$h(x,y) = h(x) + h(y)$$. And we have $$p(x,y) = p(x)p(y)$$, thus, we need to take the logarithm of $$p(x)$$ to define $$h(x)$$, i.e.

$h(x) = -log_2 p(x)$

The basis 2 is arbitrary. We also want to define information gain for a distribution. To that end, we define entropy as

$H(x) = - \sum_x p(x) log_2 p(x)$

The conditional entropy is

$H[y|x] = - \int \int p(y,x)ln p(y|x) dy dx$

and we can see

$H[x,y] = H[y|x] + H[x]$

Consider some unknown distribution p(x), and we use q(x) to approximate it. If we use q(x) to transmit the information about p(x), how much additional information is required to correct the mistake? We model that by relative entropy, or Kullback-Leibler (KL) divergence, defined as:

\begin{align} KL(p||q) & = - \int p(x) ln q(x) dx - (- \int p(x) ln p(x) dx)\\ & = - \int p(x) ln \frac{q(x)}{p(x)} dx \end{align}

Note that this is not a symmetrical quantity. We can show that $$KL(p||q) \ge 0$$ with equality if and only if $$p(x) = q(x)$$.

Recall that for two random variables x and y, if they are independent, the joint distribution can factorize into the product of their marginals, i.e. $$p(x,y) = p(x)p(y)$$. If they are not independent, how close they are from being independent? We can quantify this by mutual information between $$x$$ and $$y$$, defined as:

\begin{align} I[x,y] & = KL[p(x,y)||p(x)p(y)]\\ & = - \int \int p(x,y) ln \frac{p(x)p(y)}{p(x,y)} dx dy \end{align}

Finally, we can see

$I[x,y] = H[x] - H[x|y] = H[y] - H[y|x]$

The linearity of expectation property states that $$E[X+y]=E[X]+E[Y]$$. There is no general product form, but if X and Y are independent, we have $$E[X*y]=E[X]*E[Y]$$. The variance is defined as $$Var_P[X] = E_P[(X-E_P[X])^2]$$, or $$Var[X] = E[X^2] - (E[X])^2$$. We don't have even the linear property, but if X and Y are independent, then $$Var[X+Y]=Var[X]+Var[Y]$$. The variance generally scales as a quadratic function of X: $$Var[aX+b]=a^2Var[X]$$. The standard deviation is $$\sigma_X=\sqrt{Var[X]}$$.

### 2.4 Independence

Next, we introduce independence. First in events notation, we say that

an event $$\alpha$$ is independent of event $$\beta$$ in P, denoted as $$P \models (\alpha \bot \beta)$$, if $$P(\alpha | \beta) = P(\alpha)$$ or if $$P(\beta) = 0$$

An alternative definition

$$P \models (\alpha \bot \beta)$$ iff $$P(\alpha \cap \beta) = P(\alpha) P(\beta)$$

For conditional independence, we say that

an event $$\alpha$$ is conditionally independent of event $$\beta$$ given event $$\gamma$$ in P, denoted as $$P \models (\alpha \bot \beta | \gamma)$$, if $$P(\alpha | \beta \cap \gamma) = P(\alpha | \gamma)$$ or if $$P(\beta \cap \gamma) = 0$$

And the alternative definition as well:

$$P \models (\alpha \bot \beta | \gamma)$$ iff $$P(\alpha \cap \beta | \gamma) = P(\alpha | \gamma) P(\beta | \gamma)$$

Of course, we want to define in terms of random variables as well:

Let X, Y, Z be sets of random variables. We say X is conditional independent of Y given Z, if $$P \models (X=x \bot Y=y | Z=z)$$ for all values x,y,z. When $$Z=\emptyset$$, we simply write $$(X \bot Y)$$, and say X and Y are marginally independent.

The alternative for this:

$$P \models (X \bot Y | Z)$$ iff $$P(X,Y | Z) = P(X|Z) P(Y|Z)$$

The conditional independence says that, learning the value of Y does not provide additional information about X, once we know Z. We have some properties hold in general:

• Symmetry: $$(X \bot Y | Z) \Rightarrow (Y \bot X | Z)$$
• Decomposition: $$(X\bot Y,W | Z) \Rightarrow (X \bot Y | Z)$$
• Weak union: $$(X \bot Y,W |Z) \Rightarrow (X\bot Y | Z,W)$$
• Contraction: $$(X \bot W | Z, Y) \& (X \bot Y|Z) \Rightarrow (X\bot Y,W|Z)$$

There is one more property, intersection, that holds in positive distribution, where $$P(\alpha) > 0$$ for non-empty events. The property says, for mutually disjoint sets X,Y,Z,W, we have:

• intersection: $$X\bot Y|Z,W \& (X\bot W|Z,Y) \Rightarrow (X \bot Y,W|Z)$$.

All these properties are called graphoid axioms.

### 2.5 probability queries

After defining probability distribution, we are interested in answering probability queries. We have evidence variables E and query variables Y, and we aims to compute $$P(Y|E=e)$$, and this is called the Posterior/, also called conditional probability or posterior probability. It seems also to be called probabilistic inference, referring to the computation of posterior probabilities given evidence.

A second type of query is MAP query, also called Most Probable Explanation (MPE). It aims to find a high-probable joint assignment to some subset of variables. The MAP assignment is the most likely assignment to all of the non-evidence variables. Formally, let $$W=X-E$$, our task is to find $$MAP(W|e) = argmax_w P(w,e)$$

### 2.6 Non-parametric distributions

Non-parametric models essentially exploit the locality of data.

Histogram density model simply partitions the input into bins of width $$\delta_i$$, and calculate the density inside each bin by normalization, i.e. $$\int p(x) dx = 1$$, resulting in $$p_i = \frac{n_i}{N \delta_i}$$.

The downsides:

1. it is discontinuous
2. it does not scale with dimensionality

Two other models, kernel method and KNN, are highly related. Specifically, with some D-dimentional distance model like Euclidean, we can define a region $$R$$ of volume $$V$$ around a data point containing $$K$$ data points. The density estimator is $$p(x) = \frac{K}{NV}$$. Importantly, if we fix K and determine the value of V from the data, it is called K-Nearest-Neighbor; if we fix V and determine K from the data, it is called Kernel Method. The kernel function is the function that define the shape of the region. The kernel method has one problem: since $$V$$ is fixed, if the data points are dense at some points while sparse at others, it will lead to precision problem.

## 3 Graphical Model

### 3.1 Graph Background

We typically denote undirected graph as H, directed graph as G, and a general graph as K.

• For two nodes connected by any edge, we have neighbor (Nb) relation
• A graph in which every pair of nodes is connected by an edge is called complete

For directed graph:

• For a direct edge, we have parent (Pa) and child (Ch) relation
• The boundary of X, denoted as $$Boundary_X$$, is $$Pa_X \cup Nb_X$$
• A family in a graph is a node and its all parents
• A node is root if no parent
• A node is sink if it has no children
• A connected DAG in which every node has at most one parent is called a tree
• A connected DAG in which every node has at most one child is called a chain

For undirected graph, two nodes are adjacent if there is an edge joining them, denoted as $$X \sim Y$$. A path is a set of vertices that are adjacent sequentially. A complete graph is a graph where every pair of vertices is joined by an edge. A subgraph is a subset of vertices together with their edges. A clique is a complete subgraph. It is called maximal if it is a clique and no other vertices can be added to it and still yield a clique.

For directed graph, a path can follow any edge, but must not reverse direction. However, a trail can follow either direction. Some defines the path as a trail, i.e. can go either along or against the arrows, and define the directed path for those that go along arrows. If there exists a path between two vertices, they are said to be connected, otherwise disconnected.

An acyclic graph containing both directed and undirected edges is called a partially directed acyclic graph (PDAG). Such a PDAG can be cecomposed into chain components, where the edges between components must be directed. The hybrid network is the one that has both continuous and discrete probabilities.

The skeleton of G is the corresponding undirected graph, i.e. strip away all arrowheads.

### 3.2 Representation

We have two views of the graphical model. On one hand, we view it as a representation of a set of independence that holds in the distribution. On the other, the graph defines a skeleton for factorizing a distribution: rather than represent the entire joint distribution, we break up the distribution into smaller factors locally, and the overall joint distribution can be represented as the product of these factors. This two perspectives of graphical model are equivalent in a deep sense.

Undirected graphical model is also called Markov random field or Markov network. Directed graphical model is called Bayesian network. They differ in the set of independence they can encode, and in the factorization of the distribution they can induce.

The general model is the Naive Bayes Model, or Idiot Bayes Model, where we assume the naive Bayes Assumption that all features are conditionally independent given its class. Thus we have the factorization $$P(C,X_1,...,X_n) = P(C) \prod_{i=1}^n P(X_i|C)$$.

### 3.3 Bayesian Network (BN)

The naive Bayes model has too strong assumption. The Bayesian Network can encode the conditional independence relationship in a finer granularity. It can be viewed in two different ways, representing a joint distribution, and a compact representation of a set of conditional independence assumptions. Both views are equivalent.

The Bayesian network has two components: a graph encode the relation, and its Conditional Probability Distribution (CPD).

#### 3.3.1 Local Independence

The local independence encoded in the Bayesian Network, denoted by $$I_l(G)$$, is

For each variable $$X_i$$, we have $$(X_i \bot NonDescendants_{X_i} | Pa_{X_i}^G)$$

In other words, a node is conditionally independent of its non-descendants given its parent.

Now, we give formal semantic of a Bayesian network, through the notion of I-map. We use the notation $$I(P)$$ to denote the set of independence assertions the hold in the distribution P (ground truth). Then we can get that the local independence $$I_l(G) \subseteq I(P)$$. In other words, the graph G encodes a subset of independence that is true in the distribution. We will call G as an independency map (I-map) for P. Clearly from the definition, G must not mislead us regarding the independencies in P. The I-map concept is important because, when we use G to encode a set of conditional independence assumptions, every distribution for which G is an I-map, must satisfy these assumptions.

If P can be expressed as a product $$P(X_1,...,X_n)=\prod_{i=1}^n P(X_i | Pa_{X_i}^G)$$, we say that P factorizes according to G. This equation is called the chain rule for Bayesian network, the individual factors on the right hand side is called the local probabilistic models.

Then, importantly we have the above I-map and factorization are equivalent:

G is an I-map for P iff P factorizes according to G.

#### 3.3.2 Global Independence

Besides local independence, we can read off other independence from Bayesian Network.

There are four possible trails from X and Y via Z. When influence can flow from X to Y via Z, we say the trail is active.

• Causal trail, or causal chain, X->Z->Y encode the indirect causal effect, is active iff Z is not observed
• Evidential trail Y->Z->X encodes the indirect evidential effect, is active iff Z is not observed
• Common Cause, or causal forks, Z->{X,Y}, is active iff Z is not observed
• Common Effect, or inverted forks, {X,Y}->Z (also called v-structure), is active iff either Z or one of Z's descendants is observed. Observations on a common consequence of two independent causes tend to render those causes dependent, because information about one of the cause tends to make the other less likely, given that the consequence has occurred. This pattern is known as selection bias, or Berkson's paradox in statistics, explaining away effect in AI.

More formally

Let Z be a subset of observed variables. The trail $$X_1 \rightleftharpoons ... \rightleftharpoons X_n$$ is active given Z if

• whenever we have a v-structure, $$X_{i-1} \rightarrow X_i \leftarrow X_{i+1}$$, then $$X_i$$ or one of its descendants are in Z
• no other node along the trail is in Z

If there are more trails between X and Y, the influence can flow as long as any trail is active. More generally, if there are no active trail between any pair of nodes in two sets X and Y given Z, they are said to be directed separated (d-separated), or blocked, denoted as $$d-sep_G(X;Y|Z)$$. The set of independencies corresponding to d-separation is denoted as $$I(G)$$, as $$I(G) = \{(X \bot Y | Z) : d-sep_G(X;Y|Z)\}$$, and it is called the global Markov independencies. This time, we use I instead of $$I_l$$, without a subscript, because actually we can prove it to be sound and complete to the independencies of the distribution, $$I(P)$$. In other words, d-separation precisely encodes all independencies, i.e. $$I(G) = I(P)$$. (Note: the completeness seems to be not true, and there is a weaker definition of completeness).

(Probabilistic implications of d-separation) If sets X and Y are d-separated by Z in a DAG G, then X is independent of Y conditional on Z in every distribution compatible with G. Conversely, if X and Y are not d-separated by Z in a DAG G, then X and Y are dependent conditional on Z in at least one distribution compatible with G.

(Ordered Markov Condition) A necessary and sufficient condition for a probability distribution P to be Markov relative a DAG G is that, conditional on its parents in G, each variable be independent of all its predecessors in some ordering of the variables that agrees with the arrows of G.

(Parental Markov Condition) A necessary and sufficient condition for a probability distribution P to be Markov relative a DAG G is that every variable be independent of all its nondescendants (in G), conditional on its parents.

Actually, very different BN structure can actually encode the same set of I(G). If they do, they are called I-Equivalent. And it is impossible to decide which structure is true: there is no intrinsic property of P that would allow us to associate it with one graph rather than an equivalent one. In particular, although we can determine whether X and Y are correlated, there is no way to tell whether the correct structure is X->Y or Y->X. An entire I-equivalence class can be represented as a class PDAG.

We have a necessary condition for I-equivalent.

(Observational Equivalence) If G1 and G2 have the same skeleton and the same set of v-structures, then they are I-equivalent.

However, the reverse is not true: if they are I-equivalent, they may have different v-structures. A intuitive example is that, since a complete graph encodes empty set of conditional independence assertions, any two complete graphs are I-equivalent. But, although they have the same skeleton, they can have different v-structures. We can strengthen the condition to provide a necessary and sufficient condition, by introducing immorality:

A v-structure X->Z<-Y is an immorality if there is no direct edge between X and Y. (Such an edge is called a covering edge for the v-structure if exists).

G1 and G2 have the same skeleton and the same set of immoralities, iff they are I-equivalent.

Clearly we would like to construct a graph G that is an I-map for P, so that G can encode some independence assertions in P. However, there are many I-equivalent graphs, which one do we use? The complete graph is I-map for any distribution, thus it is a candidates. But since it is fully connected, it encodes no conditional indepencencies, thus not interesting at all. What we want is actually a minimal I-map, where removal of a single edge from the graph will render it not an I-map of P. However, the minimal I-map may fail to capture all independencies in P for sure. What we want is called a perfect map (P-map) encoded in graph K, where $$I(K)=I(P)$$. However, not every distribution has a P-map.

#### 3.3.3 Other

When choosing prior, it is important to distinguish the extremely unlikely and impossible, because one can never condition away a zero probability, no matter how much evidence we see.

Node X is conditionally independent of all other nodes in the network, given its markov blanket. (parents, children, and children's parents).

Conditional probability table (CPT) is a table in which each row shows a conditional probability.

For continuous variables, the Bayes needs to do something. Of course we can do discretization, but the precision is lost. One common solution is to define standard families of probability density functions, with a finite number of parameters, the most commonly used is the Gausion (normal) distribution. Another solution is non-parameter one. A network with both discrete and continuous variables is called hybrid Bayesian network.

### 3.4 Markov Network

The Bayesian network is parameterized by a CPD, representing the distribution over one node given others. However, this does not make sense in an undirected model, in which we want a symmetric parameterization, and the probability should capture the degree to which A and B agree or disagree. We define a factor $$\phi$$ to be a function from $$Val(D)$$ to IR, where D is a set of random variables. As with Bayesian network, we want to represent the distribution by products of local probability, thus we want to compute $$P(a,b,c,d)$$ as $$\phi_1(a,b) \phi_2(b,c) \phi_3(c,d) \phi_4(d,a)$$. However, it is by no means to be normalized, thus we need to normalize it. The normalization is not a constant obviously, but a function. In particular, $$P(a,b,c,d) = \frac{1}{Z} \phi_1(a,b) \phi_2(b,c) \phi_3(c,d) \phi_4(d,a)$$, where $$Z=\sum_{a,b,c,d} \phi_1(a,b) \phi_2(b,c) \phi_3(c,d) \phi_4(d,a)$$ is called partition function. The factors product $$\phi_1 (X,Y) \phi_2(Y,Z)$$ is defined such that the common parts in Y are matched, similar to database joins. A factor is only one contribution to the overall joint distribution, the full joint distribution must take into consideration of all factors involved. A concept called pairwise Markov Network is one where all the factors are over single variables or pairs of variables. In visual, it is structured as a grid.

When we want to make some assignment to some variables $$U=u$$, it is called condition a distribution, or factor reduction, and the operation is to remove the entries that are inconsistent with the event $$U=u$$, and re-normalize the factors. Using the same idea, given $$U=u$$ as a context, the reduced Markov Network $$H[u]$$ of the Markov Network $$H$$ is the one over nodes $$W=X-U$$, and keeps the same edges, i.e. we have an edge X-Y if there is an edge X-Y in H.

#### 3.4.1 Independence

The Markov Network also encodes a set of independence assertions, where the influence flow along undirected path. Let Z be a set of observed variables, a path is active given Z if none of the nodes in the path is in Z. Thus, this can be used to separate the graph. Formally, Z separates X and Y in H, denoted $$sep_H(X;Y|Z)$$, if there is no active path between any nodes in X and Y given Z, denoted as $$I(H) = \{(X \bot Y | Z) : sep_H(X;Y|Z)\}$$. This is the global independence, just as the d-separation defines the global independence for Bayesian network. This separation criterion is sound for detecting independence properties in distributions over H. However, it is not complete, and as in Bayesian network, there is a weaker definition of completeness that holds. The I-map definition and the relation with factorization is similar to that of Bayesian network. If P is a Gibbs distribution that factorizes over H, then H is an I-map for P. The reverse is true only for positive distributions. In other words, for positive distributions, the global independencies imply that the distribution factorizes according to the network structure, thus for this class of distributions, we have that, a distribution P factorizes over a Markov network H iff H is an I-map of P.

Bayesian network has a local independence, so as Markov network, and two of them, but only for positive distributions. The non-positive distributions allow for deterministic dependencies between variables, and such deterministic interactions can "fool" local dependence tests.

The first and weakest is the local Markov Assumption. It comes from the intuition that if two variables are directly connected, they "have the potential" of being directly correlated in a way that is not mediated by other variables. Conversely when two variables are not directly linked, there must be some way to render them conditionally independent. Specifically, the absence of an edge implies that the corresponding random variables are conditionally independent given the variables given all other variables. This is known as the pairwise independence, $$I_p(H)$$, to be defined precisely $$I_p(H) = \{(X \bot Y | \chi - {X,Y}) : X-Y \not\in H\}$$.

The second local independence is defined using Markov blanket of X in H, denoted as MBH(X), defined as the neighbors of X in H. Then, the local independencies associated with H is $$I_l(H) = \{(X \bot \chi - {X} - MB_{H}(X) | MB_{H}(X)) : X \in \chi\}$$. In other words, X is independent of the rest of the nodes in the graph given its immediate neighbors.

For general distributions, $$I_p(H)$$ is strictly weaker than $$I_l(H)$$, and strictly weaker than $$I(H)$$. For positive distributions, all three definitions are equivalent. This can be specified by three propositions:

• If $$P \models I_l(H)$$, then $$P \models I_p(H)$$
• If $$P \models I(H)$$, then $$P \models I_l(H)$$
• Let P be a positive distribution, if $$P \models I_p(H)$$, then $$P \models I(H)$$

The Markov network is often over-parameterized. It is easier to obtain a Markov network from a Bayesian network, but much harder for the reverse.

### 3.5TODO Inference

Inference refers to answering probabilistic queries, the computation of marginal vertex probabilities and expectations from their joint distribution. In particular, we study the computing of posterior probability of some variables given evidence on others.

### 3.6 Structure and Parameter Learning

Learning refers to the estimation of edge parameters from data, and learning the structure skeleton. Fundamental assumptions in learning is the Independent and Identical Distribution (IID), meaning that the data samples are sampled independently from the same distribution.

The central idea of learning is the likelihood of the data, given the model, denoted as $$P(D: M)$$. The log of it is the log-likelihood $$l(D: M) = log P(D: M)$$. The negated forms are called the loss and log-loss, reflecting the cost per instance of using the model. The hamming loss counts the number of variables in which the model differs from the ground truth.

Generally the amount of data required to estimate parameters grows linearly with the number of parameters. And since the network connectivity can be exponential to the number of variables, the amount of data can grow exponentially with the variables, or the network connectivity. The bias-variance trade-off also applies here obviously. Restricting our model class is one way to reduce overfitting, it prevents us from selecting a model that precisely captures the training data. However, we are giving more bias. The less restricted ones generally cause large variance.

Different types of objectives defines different kind of training. If we use P(Y,X) as the objective, it is called generative training, because we train the model to generate all the variables, both the predict variable and the feature variable. Alternatively, the discriminative training uses $$P(Y|X)$$ as the objective, thus only model the result. Generally speaking, generative models have a higher bias, because they make more assumptions about the form of the distribution, but obviously this training works better on limited data. The generative models offer a more natural interpretation of a domain, and are better able to deal with missing values and unlabeled data.

The training will contain three axes of problems, namely whether the output is Bayesian or Markov network, the constraints we know about the model prior to learning, and whether the data are fully observable.

#### 3.6.1 Parameter Estimation for Bayesian Network

In this setting, we consider the fixed structure, and the data set are fully observed, what we need is to estimate the parameters on top of the structure.

The central idea is Maximum Likelihood Estimation (MLE). The model is parameterized by $$\theta$$, the likelihood function is a function of $$\theta$$ and the observed data instance, characterizing the probability of the observed data under this model. Of course, we want this probability to be high, so that the model is more likely given the data. The value of $$\theta$$ that maximizes the likelihood is called the maximum likelihood estimator.

Another concept is called the sufficient statistic. It measures what features are required for estimating the likelihood, rendering other features useless. For example in coin tossing, the number of heads and number of tails are sufficient, while the order of tossing is useless. Formally, the sufficient statistic is a function from instance of $$\chi$$ to IR, such that $$L(\theta: D) = L(\theta: D')$$.

The key property to solve the MLE problem in Bayesian network is to decompose the likelihood function into products of local likelihood function, measuring how well the variable is predicted given its parents. This decomposition is called the global decomposition of the likelihood function. We can then maximize each local likelihood independently.

One problem of the MLE approach is the lack of modeling of confidence. When we observe 3 heads out of 10 tosses, we are not convinced by the bias. But once we observe 3,000 heads out of 10,000 tosses, we tend to be convinced that the coin is tricked. But the MLE will parameterize the model as the same. We introduce Bayesian parameter estimation. The fundamental fact is that, we have a lot more prior knowledge. We assumed the tosses are independent of each other, however, this assumption was made when $$\theta$$ is fixed. If we do not know $$\theta$$ in advance, then the tosses are not marginally independent, because each toss tells us something about the parameter $$\theta$$, and thereby the probability of the next toss. Thus, we can only assume that the tosses are conditionally independent given $$\theta$$. In this setting, we treat $$\theta$$ as a random variable.

More formally, now the posterior distribution over $$\theta$$ is $$P(\theta | x, ..., x[M]) = \frac{P(x, ..., x[M] | \theta) P(\theta)}{P(x, ..., x{M})}$$, where the denominator is a normalizing factor. We can see that the new posterior is the product of prior and the likelihood. If we have only a uniform prior ($$P(\theta) = 1 for \theta \in [0,1]$$), the posterior is just he likelihood function. However, it is still different from the MLE. The main philosophical difference is in the use of posterior: instead of selecting from the posterior a single value for the parameter $$\theta$$, we use it, in its entirety, for predicting the probability over the next toss.

The interesting part is choosing the prior. We want to choose the prior distribution that is compact, and efficient for update.

A Beta Distribution is parameterized by two hyperparameters, $$\alpha_1$$ and $$\alpha_0$$: $$\theta \sim Beta(\alpha_1, \alpha_0)$$ if $$p(\theta) = \gamma \theta^{\alpha_1 - 1} (1-\theta)^{\alpha_0 - 1}$$, where $$\gamma$$ is a normalizing constant, defined as $$\gamma = \frac{\Gamma(\alpha_1 + \alpha_0)}{\Gamma(\alpha_1)\Gamma(\alpha_0)}$$, and $$\Gamma(x) = \int_0^\infty t^{x-1}e^{-t}dt$$ is the Gamma function.

What's good about Beta Distribution is that, when we get more data, we get the new posterior is exactly $$Beta(\alpha_1 + M, \alpha_0 + M)$$, where M is the number of heads, M being the number of tails. The key property is that if the prior is a Beta distribution, then the posterior is also a Beta distribution. The distribution satisfying this is called to be conjugate to the Bernouli likelihood function. Another such prior is Dirichlet distribution, which generalizes the Beta distribution with K hyperparameters: $$\theta \sim Dirichlet(\alpha_1, ..., \alpha_k)$$ if $$P(\theta) \propto \prod_k \theta_k^{\alpha_k - 1}$$

#### 3.6.2 Structure Learning in Bayesian Network

If we include more edges, the model often contains spurious edges. If we include fewer edges, we may miss dependencies. When the data is limited, it is better to prefer a sparse structure.

Constraint-based structure learning try to test conditional dependence and independence in the data, then find a network that best explains these dependencies and independencies. The downside is the sensitivity to failures in individual independence tests. Since their are multiple networks having the same I-map, we often learn the I-equivalent class rather than a single network. For performance consideration, we often assume the network has bounded indegree, and our independence test procedure can perfectly answer any independence query involving a bounded number of variables. If the network is more complex, we cannot hope to learn it with a small (polynomial) number of independence queries.

The part of independence test deserves more space, while the building of the network is somehow trivial. The independence test concerns the problem of given joint samples of two variables X and Y, determine whether X and Y are independent. We often use hypothesis testing. The null hypothesis $$H_0$$ says the data were sampled from a distribution $$P(X,Y)=P(X)P(Y)$$, in other words, X and Y are independent. We want to design a procedure (called the decision rule) to decide to accept or reject the null hypothesis. This problem has two part:

1. design the decision rule: the key idea is to define a measure of deviance from the null hypothesis. A common measure is $$\chi^2$$ statistic $$d_{\chi^2} (D) = \sum_{x,y} \frac{(M[x,y] - M P(x) P(y))}{M P(x) P(y)}$$, where $$M[x,y]$$ is the actual count in the observation, $$M P(x) P(y)$$ is the expected count, and the denominator is a normalizing constant. Intuitively it measures in terms of number of mismatched data. Another deviance measure is mutual information. Once we have the deviance measure, we can design the rule as the distance is under a threshold. There is also a $$\chi^2$$ statistic for conditional independence.
2. evaluate the decision rule: The approach is to compute the probability of false rejection, i.e. the probability of seeing each particular data set if the hypothesis happens to be correct, $$P(\{D:R(D)=Reject\} | H_0, M)$$, denoted as $$p$$. The $$1-p$$ is the confidence in rejecting the hypothesis. This is called p-value, and we often use 0.95 confidence interval, or significance level, i.e. p-value of 0.05 or less.

Another family of approach is score-based structure learning, which assign a scoring function to rate how well a model fits the observed data, and turn the problem of structure learning into a model selection one, and find the one with highest possible score. However, the problem is NP-hard, and we need to find heuristic search techniques. The advantage is that it is less sensitive to individual failures, and estimate the structure as a whole.

The design of score is central to this approach, different scores also have different strategies for search. The most fundamental score is the Likelihood score, denoted as $$score_L(G, D)$$, and this score can be decomposed to local scores with parent-children relation. Indeed, the likelihood of a network measures the strength of the dependencies between variables are their parents, in other words, we prefer networks where the parents of each variable are informative about it. However, this is typically not used, because in almost all cases, the maximum likelihood network will be a fully connected one, i.e. it this score overfits the training data.

To take into consideration the prior to avoid overfitting, we design the Bayesian score, as $$score_B(G:D) = log P(D|G) + log P(G)$$. As we get more data, Bayesian score prefers the structure $$G_{X \rightarrow Y}$$ where X and Y are dependent. When the dependency between them is strong, this preference arises very quickly. But as the dependency becomes weaker, more data are required to justify this selection. Going deeper, the Bayesian score seems to be biased toward simpler structures, but as it gets more data, it is willing to recognize that a more complex structure is necessary. Therefore, it reduces the extent of overfitting. Bayesian information criterion (BIC) score, denoted as $$score_{BIC}(G:D)$$, is an approximation to Bayesian score. One important property is the score decomposability, and it allows us to conduct local search of local score for local structure, and local change to structure does not change the scores of the other parts. There is a condition for a Bayesian score to be decomposable. The popular actual prior distributions are K2 (software name) prior, BDe prior.

The search space of the models are huge, rendering the problem of searching the highest scored network NP-hard. Thus we need to define heuristic search. One fundamental step is to restrict the search space. In stead of enumerating all models, we define some moves between candidate solutions, i.e. an operation to mutate structure to another. The operations include edge addition, edge deletion, and edge reversal. These operations result in changing only one local score term. Thus, we can apply local search strategies, like greedy hill climbing, first-ascent hill climbing, basin flooding and tabu search for addressing local maxima, etc. The decomposibility of the score function is the key to reducing the computation cost.

A third approach is Bayesian model averaging, where instead of one model, it generates an ensemble of possible structures.

#### 3.6.3 Learning Markov Network

The likelihood function of Markov network contains the partition function, which is a global function over all variables. This prevents us from decomposing it. For this reason, the parameter estimation seems to have a lot of difficulties. There are no closed-form solution for parameters.

For structure learning, the constraint-based approaches seem to be easier, the independencies associated with separation in Markov network are much simpler than those with d-separation in Bayesian network. For score based approach, there are $$score_L$$, $$score_{BIC}$$, $$score_{Laplace}$$, and $$score_{MAP}$$.

#### 3.6.4 Hidden Variable

The previous discussions all assume the data are fully observed. Inclusion of hidden variables in the network can greatly simplify the structure, reducing the complexity of the network. When dealing with hidden variables, we introduce a set of observability variables, for each variable $$X_i$$, a $$O_{X_i}$$, indicating whether the variable is observed or not.

There are three kinds of missing mechanism. $$P_{missing}$$ is Missing Completely At Random (MCAR) if $$P_{missing} \models (X \bot O_X)$$. MCAR assumption is a very strong one, and is sufficient for decomposition of likelihood function. $$P_{missing}$$ is Missing At Random (MAR) if $$P_{missing} \models (O_X \bot x_{hidden}^y | x_{obs}^y)$$. The MAR assumption is a license to ignore the observation model while learning parameters.

First, consider the parameter estimation problem, where we used maximum likelihood approach. In the presence of partially observed data, we lose all of the important properties of the likelihood function: its unimodality, its closed-form representation, and the decomposition as a product of likelihoods. One approach is the gradient ascent method to go straight to optimize the likelihood. However, the lost of important properties makes it very hard.

The Expectation Maximization (EM) method is especially designed for optimizing the likelihood function in case of missing data: it tries to fill in the missing data. Originally developed by Dempster 1977-Journal-Dempster-Maximum (50,000+ citations). Indeed, this is a chicken and egg problem: learning the parameters and hypothesizing values for unobserved variables, both of them are fairly easy given the solution to the other. EM algorithm solve this chicken and egg problem using a bootstrap approach: it starts with some arbitrary starting point, and repeatedly apply the two steps, until converge. The expectation step is to use the current parameter assignment, to complete the data. The maximization step treats the completed data as observed, perform maximum likelihood estimation, and learn a new set of parameters. The algorithm guarantee that each iteration can improve the log-likelihood function, thus the process is guaranteed to converge.

In terms of structure learning, the dominate approach here is score based one. We apply laplace approximation, and use the Laplace Score.

## 4 Causality

The advantages of building DAG around causal: first, the judgments required in the construction of the model are more meaningful, more accessible, and hence more reliable. Second, the ability to represent and respond to external or spontaneous changes.

### 4.1 Stochastic Representation

The basic representation of causal model is the causal Bayesian network. We can devise the Bayesian network by quite simple steps. First, by the chain rule, the joint probability is $$P(x_1, ..., x_N) = \prod_j P(x_j | x_1, ..., x_{j-1})$$. Then, suppose that $$X_j$$ is only sensitive to some of the a small subset $$PA_j$$ of those predecessors, we have $$P(x_j | x_1, ..., x_{j-1}) = P(x_j | pa_j)$$. A minimal set $$PA_j$$ satisfying this is called the Markovian parents of $$X_j$$. It is shown by Pearl in 1988 that such $$PA_j$$ is unique if P is strictly positive. Then, combine these two equations, we have $$P(x_1, ..., x_n) = \prod_i P(x_i | pa_i)$$. Note that, although the chain rule is order specific, this equation is no longer order-specific. A distribution P is said to be Markov compatible with a DAG G, or P is Markov relative to G, if P can be factorized by this equation. The Markov compatibility is a necessary and sufficient condition for a DAG G to explain a body of data represented by P.

The action $$do(X=x)$$ means, on the graph, to delete the ingoing links to X, and set the value of X to x, and nothing else. This results in a new distribution, $$P_x(v)$$, rather than simply the original distribution conditioned on $$X=x$$. We have some intuitive properties for this new intervention distribution:

• $$P(v_i | pa_i) = P_{pa_i}(v_i)$$
• $$P_{pa_i, s}(v_i) = P_{pa_i}(v_i)$$ for all S that is disjoint of $$\{V_i, PA_i\}$$.

To test whether a variable $$X_i$$ has a causal influence on $$X_j$$, we simply compute the marginal distribution of $$X_j$$ under the action $$do(X_i=x_i)$$, i.e. $$P_{x_i}(x_j)$$ for all values $$x_i \in Val(X_i)$$, and test whether the distribution is sensitive to $$x_i$$.

### 4.2 Functional Causal Model

A functional causal model is another representation of the causal model, consisting of a set of equations of the form $$x_i = f_i(pa_i, u_i)$$, where $$pa_i$$ means parents, i.e. the immediate causes of $$X_i$$, $$U_i$$ represents the errors, i.e. the disturbances, or correlations. This general form is nonlinear, nonparametric. The linear Structural Equation Models (SEMs) are more specific: $$x_i = \sum_{k\ne i} \alpha_{ik} x_k + u_i$$.

Given a functional causal model, we can draw a graph, with arrows from each member of PA toward $$X_i$$. The resulting graph is called a causal diagram. If the causal diagram is acyclic, then the model is called semi-Markovian. If, in addition to acyclicity, the error terms are mutually independent (i.e. each variable is independent of all its non-descendants, conditional on its parents, this is also called the Markovian assumption), the model is called Markovian.

The functional causal model can also represent the intervention, by alternating a subset of functions, and most of the time, this modification is simpler, e.g. change $$x_3 = f_3(x_1, u_3)$$ to be $$x_3 = on$$. The functional model's representation of interventions offers greater flexibility and generality than that of a stochastic model:

• the analysis of intervention can be extended to cyclic model
• The interventions involving the modification of equational parameters are more readily comprehended
• The analysis of causal effects in non-Markovian models (again cyclic) will be greatly simplified
• permits the analysis of context-specific actions and policies

Actually, the biggest shine part of functional models comes at the analysis of couterfactuals. The difficulty of counterfactual queries in stochastic models lies in that given no data, it is impossible to predict non-treatment case. But, the true insights of importance of counterfactual queries rely in "integrating substantive knowledge with statistical data so as to refine the former and interpret the latter". To see that we can answer counterfactual queries, an example is given by Pearl:

Most people would agree that President Clinton's place in history would be different had he not met Monica Lewinsky, but only a few would assert that his place in history would change had he not eaten breakfast yesterday

The stochastic causal models are insufficient for computing probabilities of counterfactuals. But, a functional causal model constitutes a mathematical object sufficient for the computation and definition of such probabilities. Generally to answer a counterfactual query, three steps are generalized to any causal models:

1. abduction: update the probability $$P(u)$$ to obtain $$P(u|e)$$
2. action: do(X=x)
3. prediction: use the modified model to compute the probability of $$Y=y$$

The real reason why stochastic causal models are insufficient is the U variables do not appear explicitly in stochastic models, thus we cannot apply abduction step to update $$P(u)$$ with evidence $$e$$.

### 4.3 Causal Effect

For two disjoint set X and Y, the causal effect of X on Y is defined as P(y | do(x)), a function from X to the space of probability distribution on Y. The causal effect identifiability is defined, such that the causal effect of X on Y is identifiable from a graph G if the quantity $$P(y | do(x))$$ can be computed uniquely from any positive distribution of the observed variables. It is well known that, the causal effect is identifiable if X,Y and all parents of X are observed. Thus, for Markovian model, the causal effects are identifiable.

For semi-Markovian model, there exists some graphical test to decide whether a set Z of observed variables is sufficient for identifying $$P(y | do(x))$$. The first one being the back-door criterion:

(Back-door) A set of variables Z satisfies the back-door criterion, relative to an ordered pair ($$X_i$$, $$X_j$$) in DAG G if

1. no node in Z is descendant of $$X_i$$, and
2. Z blocks every path between $$X_i$$ and $$X_j$$ that contains an arrow into $$X_i$$

For the disjoint sets X and Y, the set Z is said to satisfy the back-door criterion relative to (X,Y) if it satisfies the criterion for any pairs $$(X_i,Y_i) \in (X,Y)$$

If Z satisfies back-door criterion relative to (X,Y), the causal effect of X on Y is identifiable, as $$P(y|do(x)) = \sum_z P(y | x, z) P(z)$$.

The second one being the front-door criterion:

(Front-door) A set of variables Z satisfies the font-door criterion, relative to an ordered pair of variables (X,Y) if

1. Z intercepts all directed paths from X to Y
2. there is no back-door path from X to Z
3. all back-door paths from Z to Y are blocked by X

If Z satisfies the front-door criterion relative to (X,Y) and if $$P(x,z)>0$$, then the causal effect of X on Y is identifiable as: $$P(y|do(x)) = \sum_z P(z | x) \sum_{x'} P(y|x',z)P(x')$$.

Typically we use do-calculus to transform the intervention from one form to another, thus it can syntactically solve intervention queries. The do-calculus is a set of inference rules.

Some more graphical notation: $$G_{\bar{X}}$$ is the graph obtained by deleting from G all arrows pointing to nodes in X, $$G_{\underline{x}}$$ the graph obtained by deleting from G all arrows emerging from nodes in X. $$P(y | \hat{x}, z) = P(y, z | \hat{x}) / P(z | \hat{x})$$ is the probability of Y=y given X is held constant at x, and that under this condition Z=z is observed.

• Insertion/deletion of observations: $$P(y | \hat{x}, z, w) = P(y | \hat{x}, w)$$ if $$(Y \bot Z | X, W)_{G_{\hat{x}}}$$. This rule reaffirms d-separation.
• Action/observation exchange: $$P(y | \hat{x}, \hat{z}, w) = P(y | \hat{x}, z, w)$$ if $$(Y \bot Z | X, W)_{G_{\hat{x}\underline{Z}}}$$. This rule provides a condition for an external intervention $$do(Z=z)$$ to have the same effect on Y as the passive observation $$Z=z$$
• Insertion/deletion of actions: $$P(y|\hat{x}, \hat{z}, w) = P(y | \hat{x}, w)$$ if $$(Y \bot Z | X, W)_{G_{\hat{X}, \hat{Z(W)}}}$$ where Z(W) is the set of Z-nodes that are not ancestors of any W-node in $$G_{\hat{X}}$$. This rule introduces or deletes an external intervention $$do(Z=z)$$

A causal effect is identifiable if there exists a finite sequence of transformations in the rules to reduce it to a standard probability expressions involving observed quantities.

The $$P(y|do(x))$$ models the total effect of X on Y. The direct effect of X on Y is given by $$P(y|do(x), do(s_{XY}))$$, where $$S_{XY}$$ is the set of all observed variables except X and Y. This effect is meant to quantify the effect that is not mediated by other variables in the model, or more accurately, the sensitivity of Y to change X while all other factors in the analysis are held fixed (to any value??).

### 4.4 Confounding

If a third variable Z can influence both X and Y, then X and Y are said to be confounded, and Z is called a confounder of X and Y, or covariates, concomitants.

There is a Simpson's Paradox for confounders. Basically, it is that an event C increases the probability of E in a given population p, and at the same time, decreases the probability of E in every subpopulation of p. In equation, it says:

• $$P(E|C) > P(E|\not C)$$: increase the probability
• $$P(E|C,F) < P(E| \not C, F)$$: decrease the probability whenever condition on F
• $$P(E|C,\not F) < P(E | \not C, \not F)$$, or condition on $$\not F$$

This essentially capture the misunderstanding between action and observation. A real world example from HackerNews: every bug you fix in your code increases your chances of shipping on time, but provides evidence that you won't. In this case, E is the event of deliver on time, C is the event of bug fix.

The problem is the first statement $$P(E|C) > P(E|\not C)$$. C is not an evidence for E, but rather a positive causal factor for E. So proper statement should be written as $$P(E | do(C)) > P(E | do(\not C))$$.

The paradox is resolved, and we result in a Sure-Thing Principle: an action C that increases the probability of an event E in each sub-population must also increase the probability of E in the population as a whole, provided that the action does not change the distribution of the sub-populations.

Finally, a formal causal definition of confounding comes that, X and Y are not confounded in causal model M iff $$P(y|do(x)) = P(y|x)$$.

### 4.5 Causal Discovery

Two books for reference

#### 4.5.1 IC algorithm

• Input: a sample distribution
• Output: a partial (mixed directed and undirected edge) DAG graph

This is very simple:

1. for each pair (a,b), search for set $$S_{ab}$$ that $$a independent b || S_{ab}$$. If no such $$S_{ab}$$, add undirected edge $$a-b$$.
2. For a-c-b, if $$c \not\in S_{ab}$$, orient the direction as a->c<-b
3. orient as many edges as we can, by two rules: (HEBI: ??)
1. the orientation should not create new v-structure (i.e. {X,Y}->Z)
2. the orientation should not create cyclic graph

So actually step 3 is unspecified. In later works by Pearl and Meek there are several rules, that are necessary and sufficient:

1. a->b whenever there exists c->a such that b and c are not adjacent (in IC* algorithm, a->b is marked with *)
2. a->b whenever there is a chain a->c->b
3. a->b if a-c->b and a-d->b and c and d are not adjacent
4. a->b if a-c->d and c->d->b and c and b are not adjacent

IC* algorithm differs in the step 3:

1. a->b with * mark whenever there exists c->a such that b and c are not adjacent
2. a->b if a and b are adjacent, and there is a directed strictly marked (with *) path from a to b.

The graph output of IC* is different. It contains four types of edges:

1. a->b
2. a-b
3. a<->b
4. a->b with * mark

IC* algorithm is not complete, in that it may not output a maximally informative graph.

#### 4.5.2 SGS algorithm

1. form complete undirected graph
2. for each pair (a,b), if we can find a subset S that can d-separate (a,b), remove a-b
3. if a-b-c, but not a-c. If a and c cannot be d-separated, orient a->b<-c
4. repeat until no more edges can be oriented
1. if a->b-c and not a-c, and there is no arrowhead at b (?), orient b->c
2. if a-b and there is a directed path from a to b, orient a->b (because we don't want cycle)

#### 4.5.3 PC algorithm

SGS requires too many d-separation test, thus computationally inefficient.

The PC algorithm differs only in step 2, how to remove edge. The trick is, we can remove an edge once we find one set of nodes to separate a and b. So it makes sense to test in some order.

PC* algorithm takes one additional step further than PC. The only difference is, when removing a-b, only test the subset of variables adjacent to a and b that are on undirected paths between a and b.

## 5 Search Algorithm

### 5.1 Branch & Bound algorithm

The problem is to minimize a function f(x) of variables $$x_1,...,x_n$$ over a region of feasible solutions S.

$min_{x\in S} f(x)$

The solutions state space S is formed as a rooted tree. The key to this algorithm is the efficient estimation of lower or upper bound. The problem is NP-hard.

f(x) is called objective function. a function g(x) is the lower bound, defines on S with the property that g(x) ≤ f(x) for all x ∈ S.

The algorithm:

1. use a heuristic, find a solution xh. Store its value B ← f(xh). B is the global best solution so far. If no solution found, init B to ∞
2. init a queue with the root ??
3. loop until the queue is empty
1. take a node N off the queue
2. if N represents a single candidate solution x (N is a leaf?) and f(x) < B, then B = f(x).
3. Else, branch on N to produce new nodes $$N_1,...,N_i$$. For each new node:
1. if g(Ni) > B, do nothing.
2. else store Ni onto the queue

Intuitively in natural language description, the problem is to minimize (or maximize) the objective function f(x) over $$x_1,..,x_n$$. The feasible solution search state space is a tree. The initial best known value is B=f(xh) or ∞ if no solution xh found by heuristic. From the root, everytime branch into two or more branches. For those branches, compute the lower bound. If the lower bound is larger than current best, then do not need to go into these branch. Thus we can eliminate the computation of this branch.

The assumption is the lower (or upper) bound is efficient to compute. Every time branch may or may not overlap, as long as the optimal solution is inside at least one branch.

### 5.2 A* algorithm

The problem is, from an initial node, find the least-cost path to one goal node (out of one or more possible goals).

$f(n) = g(n) + h(n)$

where n is current node.

• f(n) is the cost function.
• g(n) is the known cost of getting from initial node to n.
• h(n) is a heuristic esitimate of the cost to get from n to any goal node.
• h(n) must be admissible, i.e. it never overestimates the actual cost, i.e. it is always less then or equal to the actual cost.

The algorithm: from initial node, it maintains a priority queue of nodes. The lower $$f(n)$$, the higher its priority. At each step, the node with lowest $$f(x)$$ is removed, and $$f$$ and $$g$$ of its neighbors are updated. Add these neighbors into the queue. The algorithm terminates when one goal node has a lower $$f$$ value than any node in the queue.

Intuitively, from the start point, try all neighbors, and remember both the actual cost from the initial node, and the estimate from this node to one goal. Repeat trying neighbors until reach goal nodes. Stop when the goal nodes has the lowest cost function value.

### 5.3 local search

Concepts:

• plateau: a large set of neighbors having the same score

Searches

• first-ascent hill climbing: once it finds one that leads to a better score, it applies it without considering other directions

Overcome local maxima:

• basin flooding: keep track of all previous results, and consider any operator leading to result that has not been visited
• tabu search: keep a list or recent operators applied, and in each subsequent step, do not consider operators that reverse the effect of recently applied operators.
• data-perturbation: similar to random restarts. Perturb the search in a way that will allow it to overcome local obstacles and make progress toward the global maxima. Different from random restart where perturbation change the network, the data-perturbation changes the training data.

#### 5.3.1 hill climbing

Find an initial solution, which is much worse than optimal one. Attempts to find a better solution by incrementally changing a single element of the solution. Repeat until no better can be found.

#### 5.3.2 simulated annealing(SA)

Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the optimal solution.

This is essentially the key for SA: have probability to accept a move to worse state. A move from state s0 to s1 means go to that solution, and then do iteration. Stop until the energy is small enough or total budget runs out. The goal is to make the energy of the system smallest.

P(e,e',T) is acceptance probability function, which decides whether to move from e to e'. Well, if e'<e, then it should be 1, so that always goes to smaller state if found. But this is not required.

Go alone the decrease of the gradient.

## 6 Similarity metrics

Jaccard index, also known as the Jaccard similarity coefficient, compares two sets, A and B.

$$J(A,B) = \frac{A \cap B}{A \cup B}$$

Cosine similarity is most widely used, typically the terms are weighted with TF-IDF. term frequency–inverse document frequency (TF-IDF) is used in information retrieval. It is a numerical statistic that is intended to reflect how important a word is to a document.

• term frequency:: the number of times a term occurs in a document. E.g. "good" appears 3 times.
• inverse document frequency:: this is to fix the word "the" appears so often and not that useful. $$idf(t,D) = log \frac{N}{|\{d \in D : t \in d\}}$$ Meaning the total number of documents, divided by the documents that contains the word t.
• term frequency–inverse document frequency:: tfidf(t, d) = tf(t, d) * idf(t, D)

Example:

term doc 1 doc 2
this 1 1
is 1 1
a 2
sample 1
another   2
example   3
• tf("this", d1) = 1/5
• tf("this", d2) = 1/7
• idf("this", D) = log(2/2) = 0
• tfidf("this", d1) = 1/5 * 0 = 0
• tfidf("this", d2) = 1/7 * 0 = 0

Both the tfidf is 0, meaning "this" is not very informative.

• tf("example", d1) = 0 / 5
• tf("example", d2) = 3/7
• idf("example", D) = log(2/1) = 0.3
• tfidf("example", d1) = 0 / 5 * 0.3 = 0
• tfidf("example", d2) = 3 / 7 * 0.3 = 0.13