December 2018
Intermediate to advanced
764 pages
18h 18m
English
book
Python: Advanced Guide to Artificial Intelligence
by Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani
Content preview from Python: Advanced Guide to Artificial Intelligence
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Bayesian Networks and Hidden Markov Models
In this chapter, we're going to introduce the basic concepts of Bayesian models, which allow working with several scenarios where it's necessary to consider uncertainty as a structural part of the system. The discussion will focus on static (time-invariant) and dynamic methods that can be employed where necessary to model time sequences.
In particular, the chapter covers the following topics:
- Bayes' theorem and its applications
- Bayesian networks
- Sampling from a Bayesian network using direct methods and Markov chain Monte Carlo (MCMC) ones (Gibbs and Metropolis-Hastings samplers)
- Modeling a Bayesian network with PyMC3
- Hidden Markov Models (HMMs)
- Examples with hmmlearn
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.