What you need for this book
by Abinash Panda, Ankur Ankan
Mastering Probabilistic Graphical Models Using Python
- Mastering Probabilistic Graphical Models Using Python
- Table of Contents
- Mastering Probabilistic Graphical Models Using Python
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Bayesian Network Fundamentals
- 2. Markov Network Fundamentals
- 3. Inference – Asking Questions to Models
- 4. Approximate Inference
- The optimization problem
- The energy function
- Exact inference as an optimization
- The propagation-based approximation algorithm
- Propagation with approximate messages
- Sampling-based approximate methods
- Forward sampling
- Conditional probability distribution
- Likelihood weighting and importance sampling
- Importance sampling
- Importance sampling in Bayesian networks
- Markov chain Monte Carlo methods
- Gibbs sampling
- The multiple transitioning model
- Using a Markov chain
- Collapsed particles
- Collapsed importance sampling
- Summary
- 5. Model Learning – Parameter Estimation in Bayesian Networks
- 6. Model Learning – Parameter Estimation in Markov Networks
- 7. Specialized Models
- Index
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