Probabilistic Inference

Let's now move ahead with the theme Reasoning under Uncertainty, and see how probability can be used for inference in various AI problems.

Bayes rule and probabilistic inference

Bayes rule has a central role in statistical inference and machine learning. The basis for this is that it can be applied in scenarios where one of the random variables in the model corresponds to an unknown "state" which we are interested to learn. If the model includes other variables that correspond to "observations" that can be made and that are dependent on the unknown state, we can use the Bayes rule as follows

The left side of the equation is called the posterior probability (probability after the observation). The first factor on the right is called the prior probability (probability prior to the observation), the middle factor is called the likelihood (how likely the observation is when the state is given), and the last term on the right has many names, of which the annoying denominator may be the most fitting. (Evidence and marginal likelihood are more common.)

     R = P(State=1 | obs) / P(State=2 | obs)

     P(State=1) P(obs | State=1) / P(obs)   P(State=1) P(obs | State=1)
R =  ------------------------------------ = ---------------------------
     P(State=2) P(obs | State=2) / P(obs)   P(State=2) P(obs | State=2)

  P(State=1 | obs) = R / (1+R)

Naive Bayes classification

One of the most useful applications of the Bayes rule is the so called naive Bayes classifier. It is a machine learning technique that can be used to classify objects such as text documents into two or more classes. The classifier is trained by analysing a set of training data, for which the correct classes are given.

The naive Bayes model is a probabilistic model that involves a class variable -- this corresponds to the state variable above -- and a number of feature variables. The assumption in the model is that the feature variables are conditionally independent given the class. (We will not discuss the exact meaning of conditional independence on this course. You can find more about it from the literature. For our purposes, it is enough to be able to exploit conditional independence in building the classifier.)

We will use a spam email filter as a running example for illustrating the idea of the naive Bayes classifier. Thus, the class variable indicates whether a message is spam (or "junk email") or whether it is a legitimate message (also called "ham"). The words in the message correspond to the feature variables, so that the number of feature variables in the model is determined by the length of the message.

The naive Bayes model can be represented as a Bayesian network, which encodes the conditional independence between the feature variables (in this case, the words of the email message) given the class variable as in the above diagram. We'll return to Bayesian networks in the next section below.

Estimating parameters

To define the naive Bayes model, we need to specify the distribution of each variable. For the class variable, this is the distribution of spam vs ham messages, which we can for simplicity assume to be 1:1, i.e., P(spam) = P(ham) = 0.5.

For the feature variables, we will define two distributions: one for the spam messages and another one for the ham messages. In each case, we will make the simplifying assumption that each of the words in the message is distributed according to the same distribution. And as we said above, we also assume that the words are independent of each other given the spam/ham class.

The word distributions for the two classes are best estimated from actual training data, i.e., a corpus of spam messages and a corpus of legitimate messages. The simplest way is to count how many times each word, aardvark, aardwolf, ..., Zyzzogeton, appears in the corpus and dividing the number by the total number of words in the corpus.

To illustrate the idea, let's assume that we have at our disposal both a spam corpus and a ham corpus. You can easily obtain one by saving a batch of your emails in two files.

Assume that we have calculated the number of occurrences of the following words in the two classes of messages:

        word    spam     ham
 ----------- ------- -------
 million         156      98
 dollars          29     119
 adclick          51       0
 conferences       0      12
 ----------- ------- -------
       total   95791  306438

We can now estimate that the probability that a word in a spam message is million, for example, is about 156/95791 ≈ 0.0016285. In other words, on the average, roughly every 614th word in a spam message is million. Likewise, we get the estimate 0.0003198 for the probability that a word in a ham message is million. Both of these probability estimates are small but more importantly, the former is higher than the latter. This turns out to be a sign that the word in question hints towards the message being spam -- which sounds logical. Words for which the ratio of the probabilities is the other way around, hint towards the message being ham.

Classifying new data

After the parameters of the model have been estimated, the classifier is ready for use! When we are given a new message, we will compute the posterior probability P(spam | message), where message is a place-holder for the words in the message, for example, P(spam | 'million', 'conferences') (which would be a really short message).

Let's compute the posterior probability

     P(spam | Word1 = 'million', Word2 = 'conferences')

which is abbreviated without a great risk of confusion -- but please note that unlike million and conferences, which are hyphenated ('like this') to indicate that they are words that occur in the message, the term spam indicates the class of the message, not the word "spam" -- as

     P(spam | 'million', 'conferences')

By Bayes rule, we have

                                      P(spam) P('million', 'conferences' | spam)
 P(spam | 'million', 'conferences') = ------------------------------------------
                                            P('million', 'conferences')

The numerator on the right side is the likelihood term and its meaning is the probability that the first two words are million and conferences given that the message is spam. The denominator on the right side -- it's the annoying one -- is the probability that the first two words are as stated when the spam/ham status of the message is not given.

Let's look at the likelihood term first. The conditional independence assumption, i.e., the naive Bayes assumption, implies that the likelihood can be "factorized" as follows

  P('million', 'conferences' | spam) = P('million' | spam) P('conferences' | spam)

This is very useful since these are the kind of probabilities we have estimated from the data. The same factorization rule can be applied no matter how many words there are in the message.

Next up is the annoying denominator. However, we'll use the hint above and avoid calculating the denominator (at least explicitly). To achieve this, we consider the ratio of posteriors:

 P(spam | 'million', 'conferences')   P(spam) P('million', 'conferences' | spam) / Z
 ---------------------------------- = ----------------------------------------------
  P(ham | 'million', 'conferences')    P(ham) P('million', 'conferences' | ham) / Z

where Z, defined as Z = P('million', 'conferences'), is the annoying denominator which cancels out just as promised in the hint.

The term P('million', 'conferences' | ham) is treated in the exact same manner as the term corresponding term for spam (see above), and the numbers required to calculate its value are available since we have estimated them from the training data.

The following exercises will demonstrate the use of the naive Bayes model.

Implementation details

The above exercise should make it relatively straightforward to see how the filter can be implemented by programming: the key insight is the routine nature of the word-by-word calculations.

A convenient way to group the computations is obtained by slight reshuffling of the terms. Consider still our example two-word message, and the following formula for the ratio of the posterior probabilities

     P(spam | 'million', 'conferences') 
 R = ----------------------------------  
      P(ham | 'million', 'conferences')

     P(spam) P('million' | spam) P('conferences' | spam)
   = ---------------------------------------------------
       P(ham) P('million' | ham) P('conferences' | ham)

Since the ratio of products is equal to the product of ratios -- i.e., for example, (ABC)/(DEF) = (A/D)(B/E)(C/F) -- we can group the above terms as

     P(spam)  P('million' | spam)  P('conferences' | spam)
 R = -------  -------------------  -----------------------
     P(ham)   P('million' | ham)   P('conferences' | ham)

More generally, the formula is

       P(spam)  N  P(word_i | spam)
   R = -------  Π  ----------------
       P(ham)  i=1 P(word_i | ham)

where N is the number of words in the message, and word_i is the i'th word in the message.

Intuitively, the above formula encodes the rule that any word whose probability is higher in spam messages than in ham messages will increase the ratio R, and vice versa. For example, in our two-word message, for i=1, we have word_i = 'million', and the ratio of the word probabilities is given by

    P(word_1 | spam)   P('million' | spam)   0.0016285
    ---------------- = ------------------- ≈ --------- ≈ 5.1 
    P(word_1 | ham)    P('million' | ham)    0.0003198

Thus, each occurrence of the word 'million' in the message leads to roughly a five-fold increase in the posterior probability ratio.

Putting things together, here's a pseudocode sketch of the classification stage where we assume that the probability estimates have already been calculated from training data.

1:  spamicity(words, estimates):
2:     R = estimates.prior_spam / estimates.prior_ham
3:     for each w in words:
4:        R = R * estimates.spam_prob(w) / estimates.ham_prob(w)
5:     return R

Here estimates is an object that stores the estimated probabilities: estimates.prior_spam is the probability that a message is spam, P(spam), estimates.prior_ham the same for ham; estimates.spam_prob(w) is the probability P(Word = w | spam), and estimates.ham_prob(w) the same for ham.

                                       N
   logR = log(P(spam)) - log(P(ham)) + Σ [log(P(word_i | spam)) - log(P(word_i | ham))]
                                      i=1

Here is some additional material on naive Bayes spam filtering that you may like to take a look at:

• Dr Dobbs Journal: Naive Bayesian text classification
• M. Sahami, S. Dumais, D. Heckerman, E. Horvitz (1998). "A Bayesian approach to filtering junk e-mail". AAAI'98 Workshop on Learning for Text Categorization.

Bayesian networks

As we mentioned above, the naive Bayes model is a kind of a Bayesian network, with an associated graph structure where the class variable is the root and the only "parent" of all the feature variables.

Bayesian networks are a model family which belong to the still more general category of (probabilistic) graphical models, which are a modeling "language" for probabilistic models. The power of graphical models is mainly due to i) their ability to compactly represent multivariate domains with complex dependencies, and ii) efficient inference procedures that exploit the structure of the models.

Building blocks of a Bayesian network

A Bayesian network will represent a joint distribution over a number of random variables. Depending on the complexity of the network (which depends on the number of placement of the edges in the graph), the Bayesian network representation may require significantly fewer numerical parameters than the number of elementary events in the domain.

The structure of the network is encoded as a graph where each random variable is represented as a node, and the dependency structure is characterized by a set of directed edges between the nodes. The graph must be a directed acyclic graph (DAG), which means that it is impossible to start from a node follow a path in the direction of the edges to arrive at the same node.

Here's an example DAG:

The random variables in the model are X,Y,Z, and Å. (For the non-Scandi students, the last one is what we call the "Swedish O" in Finland; see Wikipedia:Å. It conveniently comes after X, Y, and Z in our alphabet.) The edges correspond to direct dependency, so that the absence of an edge between any two variables means that the variables are conditionally independent given some (possibly empty) set of other variables. The details of this are slightly too complicated to discuss here, so we'll not go into them. For now, it is enough to remember that the edges are a way to encode the dependency structure in the domain.

In addition to the structure encoded as the DAG, a Bayesian network comes with a set of parameters quantifying the conditional distributions of the variables. These are given in the form P(V=v | Pa_V = pa_V), where V is a random variable and v is a value, and Pa_V denotes the parents of node V in the graph: i.e., the nodes from which there is an edge to V. For example, in the above DAG, we have Pa_Z = {X,Y}, and Pa_Y = ∅ because node Y has no parents at all.

When the variables are categorical, i.e., they take values in a finite set, the conditional distributions can be specified as conditional probability tables (CPTs). A CPT for variable V includes the distribution of V for each possible combination of the values of V's parents. We call such combinations parent configurations. For example, if in the above example network, all variables are binary, then the parent configurations of Z are (X=0, Y=0), (X=0, Y=1), (X=1, Y=0), (X=1, Y=1), and the CPT of Z will have four rows, each containing a conditional distribution for Z. A concrete example is as follows

So for instance, if X=1 and Y=0, then the conditional probability of Z=1 is given by P(Z=1 | X=1, Y=0) = 0.5, and so on.

For a variable that has no parents in the network, the CPT is actually just a single distribution, and the CPT has only one row. For variables that have many parents, the CPT may easily become very very large.

To make things concrete, we use a running example which is the car Bayesian network discussed at the lecture. Its structure is the following:

The variables in the model are abbreviated as the first letters:

    [B]attery
    [R]adio
    [I]gnition
    [G]as
    [S]tarts
    [M]oves

The CPTs are as follows:

Just to take an example, the car will start (S=1) with probability 0.99 in case the ignition works (I=1) and there is gasoline in the tank (G=1). (No Tesla obviously). If there's a problem either with the ignition or the gasoline, then the car won't start for sure, i.e., P(S=1 | I=i, G=g) = 0.0 for all other combinations (i,g) than (1,1).

The structure (the DAG) and the parameters in the CPTs are the building blocks of a Bayesian network. Next we dicuss how they represent a joint distribution and how they can be used to perform inference about the domain.

Defining a probabilistic model as a Bayesian network

A probabilistic model is really nothing but a joint distribution over the random variables in the domain. The joint distribution defines the probability of each elementary event, which are equivalent to joint value assignments for the random variables. In the car domain, an elementary event is defined by six bits, B,R,I,G,S,M. For example, the event 1,1,1,1,1,1 would be a good day: the battery is alive, you listen to some good music, the car starts because you remembered to fill the tank yesterday, and you make it to the lecture in time.

The joint probability of any elementary event is obtained as the product of the conditional probabilities of the form P(V=v | Pa_V = pa_V), where you'll recall Pa_V refers to the parent set of V. In the car domain, the product is thus of the form:

P(B=b,R=r,I=i,G=g,S=s,M=m) = P(B=b) P(R=r | B=b) P(I=i | B=b) P(G=g) P (S=s | I=i,G=g) P(M=m | S=s),

where b,r,i,g,s, and m denote values of the respective random variables. The above formula is called a factorization of the joint probability.

The probability of a good day with the car is

P(B) P(R|B) P(I|B) P(G) P(S|I,G) P(M|S) = 0.9 × 0.9 × 0.95 × 0.95 × 0.99 × 99 ≈ 0.716.

Pay close attention to the form of the terms, and make sure you could choose the right values from the above CPTs to do the above calculation.

Or, some days are fine except the radio may not work, which is denoted as ¬R. The probability of the elementary event where everything else is good but the radio is out is obtained similarly, and the result is:

P(B) P(¬R|B) P(I|B) P(G) P(S|I,G) P(M|S) ≈ 0.080.

Check that you get the same result by substituting the numbers from the CPTs in the correct places. Remember that ¬R means R=0 (see the hint above).

You may ask what the point in all this business with the CPTs is: having to come up with all the numbers (parameters) in the CPTs looks like a lot of work. Why not just directly try to come up with the probabilities of the elementary events if that is the end result? The answer is that we can sometimes save a whole lot of work this way. In the car example, the CPTs contain 2+4+4+2+8+4 = 24 numbers, of which half are actually redundant since the numbers on each row must always sum up to one, i.e., there are really only 12 numbers to choose. The number of elementary events, on the other hand, is 2⁶=64, i.e., we would have to come up with 63 parameters (the last one could be obtained by subtracting the others from 1.0). This shows how Bayesian networks can lead to compact representation of probabilistic models.

Exact inference in Bayesian networks

Compact representation of probabilistic models is only one of the two main advantages of Bayesian networks. The other advantage is efficient inference. Here we will discuss what we mean by inference, but to get to the efficient inference algorithms, you will have to study a bit more than this course.

Just like in the above examples where we used Bayes rule and other probability calculus methods to compute posterior probabilities of the kind P(state | observations), we can do the same with Bayesian networks. Continuing with the car example, we can for example compute P(battery dead | radio doesn't work, car won't move), or P(¬B | ¬R, ¬M), to figure out what is wrong with our car.

So, let's get to work. We can start by writing the conditional probability as a ratio of two probabilities

                   P(¬B, ¬R, ¬M)
  P(¬B | ¬R, ¬M) = -------------
                      P(¬R, ¬M)

We can now obtain the probability of each of the above eight elementary events by using the factorization into a product of conditional probabilities, and substituting the appropriate values into the product from the CPTs. Take the first one:

P(¬B, ¬R, I, G, S, ¬M) = 0.1 × 1.0 × 0.0 × 0.95 × 0.0 × 1.0 = 0.0

which turns out to be equal to zero. Can you see why this makes sense? (Remember the meaning of ¬B and I. Can they happen at the same time?)

The denominator in the formula for the posterior probability we wish to compute, P(¬R, ¬M), can also be evaluated using the same technique. It will involve a sum of 16 terms since there are four variables that we need to sum over when we expand the event into a sum of elementary events. But if we work hard and do the math, we'll get the answer we need.

We call this the brute-force approach, and it has the advantage that it always leads to exactly the right answer. Implementing the calculations in a computer avoids the tedious task of writing down large sums and manually calculating them. However, in larger domains than our small car example, the calculations will become infeasible even for a computer. The problem is that the number of terms to be evaluated grows exponentially with respect to the number of variables in the model. That is why the more advanced and efficient inference algorithms that we've alluded to a few times are such an important thing. Another solution is to use approximate inference, which is our next topic.

Generating data from a Bayesian network

In order to do approximate inference, we need to be able to generate (or sample) data from the joint distribution defined by a Bayesian network. The data will consist of tuples, each of which is essentially a list of values for each of the random variables in the domain. Thus, each tuple corresponds one-to-one to an elementary event ω.

The algorithm to generate tuples is quite simple. We first choose values for any variables that have no parents. The choice is done randomly according to the distribution defined by the CPT. For example, to choose the value for the Battery variable in the car model, we use the distribution (0.1, 0.9). Similarly, the value of variable Gas is drawn from the distribution (0.05, 0.95).

To choose values for the remaining variables, we proceed in such an order that when dealing with a variable, we have already chosen the value(s) for its parent(s). After choosing the value for Battery, we can thus move on to variable Radio. The distribution from which we choose the value of a child variable (such as Radio) is determined by the configuration of its parents (such as Battery). If B=1, then we draw the value of R from distribution (0.1, 0.9). Whereas if B=0, then we draw the value of R from distribution (1.0, 0.0) and the outcome is always R=0. No battery, no music.

Note that when choosing the value of Starts, we need to chosen the values of both Ignition and Gas.

Pseudocode for a generating n random tuples from a Bayesian network is as follows.

1: generate_tuples(n, model):
2:
   for i = 1 to n:
3:       v = empty array
4:       for V in model.variables:
5:          pa = v[V.Pa]    # parents of V
6:          v.append(sample(V.CPT(pa)))
7:       output v


In the pseudocode, model is an object that defines the Bayesian network model by giving for each variable V a set of parents (in the form of a set of indices that can be used to select variables in array v), and the corresponding CPTs. The selection operator v[V.Pa] extracts from the array the elements that define the values of the parents of V. These are used on line 6 to choose the correct row from the CPT of V. Note that here it is important that the variables be processed in such an order that the parent values are added into the array before they are needed for generating the children.

Approximate inference (Monte Carlo)

The simplest approximate inference method is based on using the tuples sampled from a Bayesian network to estimate probilities directly as relative frequencies. To estimate the probability of an event, we simply draw a large number n of tuples, and count in how many of them the event occurs. The number of occurrences divided by n is then an estimate of the probability of the event.

Thus, we get

                    #{tuples where B=0, R=0, M=0}
    P(¬B, ¬R, ¬M) ≈ -----------------------------
                                  n

To approximate conditional probabilities, we simply write them as ratios, P(A | B) = P(A, B) / (B), and apply the same estimator to both the numerator and the denominator.

This is a very simple and general technique, which belongs to the category of Monte Carlo techniques due to the use of chance in obtaining the results. The downside is that the result is not exact. The variance that is caused by the random nature of the process can sometimes make the obtained probabilities quite inaccurate. The bigger the number of tuples, however, the better the accuracy, and in the limit as n → ∞ the approximation converges to the exact value.