SKEDSOFT

Introduction: Our development of statistical learning methods begins with the simplest task: parameter learning with complete data. A parameter learning task involves finding the numerical parameters for a probability model whose structure is fixed. For example, we might be interested in learning the conditional probabilities in a Bayesian network with a given structure. Data are complete when each data point contains values for every variable in the probability model being learned. Complete data greatly simplify the problem of learning the parameters of a complex model. We will also look briefly at the problem of learning structure.

Maximum-likelihood parameter learning: Discrete models
Suppose we buy a bag of lime and cherry candy from a new manufacturer whose lime–cherry proportions are completely unknown—that is, the fraction could be anywhere between 0 and 1. In that case, we have a continuum of hypotheses. The parameter in this case, which we call , is the proportion of cherry candies, and the hypothesis is h. (The proportion of limes is just 1 - .) If we assume that all proportions are equally likely a priori, then a maximumlikelihood approach is reasonable. If we model the situation with a Bayesian network, we need just one random variable, Flavor (the flavor of a randomly chosen candy from the bag). It has values cherry and lime, where the probability of cherry is  (see Figure 20.2(a)). Now suppose we unwrap N candies, of which c are cherries and `=N - c are limes. According to Equation (3), the likelihood of this particular data set is

The maximum-likelihood hypothesis is given by the value of  that maximizes this expression. The same value is obtained by maximizing the log likelihood,

(By taking logarithms, we reduce the product to a sum over the data, which is usually easier to maximize.) To find the maximum-likelihood value of , we differentiate L with respect to and set the resulting expression to zero:

In English, then, the maximum-likelihood hypothesis hML asserts that the actual proportion of cherries in the bag is equal to the observed proportion in the candies unwrapped so far! It appears that we have done a lot of work to discover the obvious. In fact, though, we have laid out one standard method for maximum-likelihood parameter learning:
1. Write down an expression for the likelihood of the data as a function of the parameter(s).
2. Write down the derivative of the log likelihood with respect to each parameter.
3. Find the parameter values such that the derivatives are zero.

Figure 20.2 (a) Bayesian network model for the case of candies with an unknown proportion of cherries and limes. (b) Model for the case where the wrapper color depends (probabilistically) on the candy flavor.

The trickiest step is usually the last. In our example, it was trivial, but we will see that in many cases we need to resort to iterative solution algorithms or other numerical optimization techniques, as described in Chapter 4. The example also illustrates a significant problem with maximum-likelihood learning in general: when the data set is small enough that some events have not yet been observed—for instance, no cherry candies—the maximum likelihood hypothesis assigns zero probability to those events. Various tricks are used to avoid this problem, such as initializing the counts for each event to 1 instead of zero.

These results are very comforting, and it is easy to see that they can be extended to any Bayesian network whose conditional probabilities are represented as tables. The most important point is that, with complete data, the maximum-likelihood parameter learning problem for a Bayesian network decomposes into separate learning problems, one for each parameter. The second point is that the parameter values for a variable, given its parents, are just the observed frequencies of the variable values for each setting of the parent values. As before, we must be careful to avoid zeroes when the data set is small.