Parameter learning

In the previous sections, we have been discussing the general concepts related to learning. Now, in this section, we will be discussing the problem of learning parameters. In this case, we will already know the networks structure and we will have a dataset, , of full assignment over the variables. We have two major approaches to estimate the parameters, the maximum likelihood estimation and the Bayesian approach.

Maximum likelihood estimation

Let's take the example of a biased coin. We want to predict the outcome of this coin using previous data that we have about the outcomes of tossing it. So, let's consider that, previously, we tossed the coin 1000 times and we got heads 330 times and got tails 670 times. Based on this observation, we can define a parameter, , which represents our chances of getting a heads or a tails in the next toss. In the most simple case, we can have this parameter, , to be the probability of getting a heads or tails. Considering to be the probability...