Mastering Probabilistic Graphical Models Using Python

Bayesian parameter estimation

In the preceding section, we discussed the method of estimating parameters using the maximum likelihood, but as it turns out, our maximum likelihood method has a lot of drawbacks. Let's consider the case of tossing a fair coin 10 times. Let's say that we got heads three times. Now, for this dataset, if we go with maximum likelihood, we will have the parameter, , but our prior knowledge says that this should not be true. Also, if we get the same results of tossing with a biased coin, we will have the same parameter values. Maximum likelihood fails in accounting for the situation where, because of our prior knowledge, ...