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

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Chapter 5. Model Learning – Parameter Estimation in Bayesian Networks

So far in our discussion, we have always considered that we already know the network model as well as the parameters associated with the network. However, constructing these models requires a lot of domain knowledge. In most real-life problems, we usually have some recorded observations of the variables. So, in this chapter, we will learn to create models using the data we have.

To understand this problem, let's say that the domain is governed by some underlying distribution, Model Learning – Parameter Estimation in Bayesian Networks. This distribution is induced by the network model, . Also, we are provided with a dataset, of M samples. ...

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