Mastering Probabilistic Graphical Models Using Python

Finding the most probable assignment

In the previous section, we computed the maximum unnormalized probability value, but for MAP, we need to compute the states of the variables corresponding to the one in which this value occurs. Taking our earlier example of the network A → B, we first computed , but the state of the variable B for which P(b|a) gives the maximum value also depends on the state of the variable A. So, we will first need to compute and then compute the state of B accordingly. So, from the CPDs of the network, we can see that . Now ...