Mastering Probabilistic Graphical Models Using Python

Chapter 4. Approximate Inference

In the previous chapter, we saw algorithms for exact inference on graphical models. The computational complexity of calculating exact inference is exponential to the tree width of the network. Hence, for much larger networks whose tree width is large, exact inference becomes infeasible. Also, in many of our real-life problems, we are not particularly concerned about the exact probabilities of random variables. Rather, we are much more interested in the relative probabilities of the states of variables. Therefore, in this chapter, we will discuss algorithms to perform approximate inference over networks. There are many algorithms for approximate inference, but the approach to find an approximate distribution remains ...