O'Reilly logo

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

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Bayesian and Markov networks

Until now, we have discussed two different models for representing graphical models. Each of these can represent independence constraints that the other cannot. In this section, we will look at the relationship between these two models.

Converting Bayesian models into Markov models

Both Bayesian models and Markov models parameterize a probability distribution using a graphical model. Further, these structures also encode the independencies among the random variable. So, when converting a Bayesian model into a Markov one, we have to look from the following two perspectives:

  • From the perspective of parameterization, that is, representing the probability distribution of the Bayesian model using a fully parameterized Markov ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required