6 - Probabilistic Models: From Simple to Complex

Summary

The sixth chapter provides a deeper exploration of probabilistic models, building upon concepts encountered earlier in the text. The chapter illustrates how to construct diverse models, particularly by employing the notion of conditional independence. It also outlines standard methods for estimating parameters and hidden states, as well as techniques for sampling. The chapter concludes by discussing and implementing applications such as Kalman filtering and Gibbs sampling. The chapter covers a range of topics, including parametric families of probability distributions, maximum likelihood estimation, modeling complex dependencies using conditional independence and marginalization, and applications such as linear-Gaussian models and Kalman filtering.