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Building Probabilistic Graphical Models with Python by Kiran R Karkera

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Chapter 5. Parameter Learning

In this chapter, we will learn about the methods to estimate the parameters of a PGM. We start with toy examples such as estimating the bias in a coin flipping experiment.

Our journey, so far, on the PGM trail can be compared to the task of a sales person (let's call him Jake) trying to sell a software package to a large scale company. He may attempt to identify the different people involved, such as the end users of the software, managers, and the procurement department, among others. This is akin to finding random variables (including latent or hidden random variables) in a graphical model. Jake will try to make connections with people who could be interested or by identifying, for example, who influences whom, who ...

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