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Mastering Probabilistic Graphical Models Using Python by Abinash Panda, Ankur Ankan

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Chapter 1. Bayesian Network Fundamentals

A graphical model is essentially a way of representing joint probability distribution over a set of random variables in a compact and intuitive form. There are two main types of graphical models, namely directed and undirected. We generally use a directed model, also known as a Bayesian network, when we mostly have a causal relationship between the random variables. Graphical models also give us tools to operate on these models to find conditional and marginal probabilities of variables, while keeping the computational complexity under control.

In this chapter, we will cover:

  • The basics of random variables, probability theory, and graph theory
  • Bayesian models
  • Independencies in Bayesian models
  • The relation between ...

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