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  • Book Overview & Buying Mastering Probabilistic Graphical Models with Python
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Mastering Probabilistic Graphical Models with Python

Mastering Probabilistic Graphical Models with Python

By : Ankur Ankan
3.3 (7)
Buy this Book
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Mastering Probabilistic Graphical Models with Python

Mastering Probabilistic Graphical Models with Python

3.3 (7)
By: Ankur Ankan
Buy this Book

Overview of this book

Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples.
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Bayesian Network Fundamentals
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1. Bayesian Network Fundamentals
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Probability theory
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Installing tools
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Representing independencies using pgmpy
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Representing joint probability distributions using pgmpy
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Conditional probability distribution
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Graph theory
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Bayesian models
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Relating graphs and distributions
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CPD representations
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Summary
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2. Markov Network Fundamentals
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2. Markov Network Fundamentals
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Introducing the Markov network
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The factor graph
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Independencies in Markov networks
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Constructing graphs from distributions
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Bayesian and Markov networks
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Summary
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3. Inference – Asking Questions to Models
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3. Inference – Asking Questions to Models
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Inference
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Variable elimination
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Belief propagation
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MAP using variable elimination
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Factor maximization
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MAP using belief propagation
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Finding the most probable assignment
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Predictions from the model using pgmpy
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A comparison of variable elimination and belief propagation
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Summary
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4. Approximate Inference
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4. Approximate Inference
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The optimization problem
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The energy function
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Exact inference as an optimization
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The propagation-based approximation algorithm
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Propagation with approximate messages
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Sampling-based approximate methods
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Forward sampling
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Conditional probability distribution
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Likelihood weighting and importance sampling
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Importance sampling
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Importance sampling in Bayesian networks
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Markov chain Monte Carlo methods
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Gibbs sampling
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The multiple transitioning model
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Using a Markov chain
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Collapsed particles
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Collapsed importance sampling
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Summary
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5. Model Learning – Parameter Estimation in Bayesian Networks
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5. Model Learning – Parameter Estimation in Bayesian Networks
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General ideas in learning
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Learning as an optimization
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Discriminative versus generative training
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Parameter learning
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Bayesian parameter estimation
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Structure learning in Bayesian networks
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The Bayesian score for Bayesian networks
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Summary
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6. Model Learning – Parameter Estimation in Markov Networks
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6. Model Learning – Parameter Estimation in Markov Networks
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Maximum likelihood parameter estimation
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Summary
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7. Specialized Models
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7. Specialized Models
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The Naive Bayes model
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Dynamic Bayesian networks
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The Hidden Markov model
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Applications
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Summary
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Index
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Index

Installing tools

Let's now see some coding examples using pgmpy, to represent joint distributions and independencies. Here, we will mostly work with IPython and pgmpy (and a few other libraries) for coding examples. So, before moving ahead, let's get a basic introduction to these.

IPython

IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, which offers enhanced introspection, rich media, additional shell syntax, tab completion, and a rich history. IPython provides the following features:

  • Powerful interactive shells (terminal and Qt-based)
  • A browser-based notebook with support for code, text, mathematical expressions, inline plots, and other rich media
  • Support for interactive data visualization and use of GUI toolkits
  • Flexible and embeddable interpreters to load into one's own projects
  • Easy-to-use and high performance tools for parallel computing

You can install IPython using the following command:

>>> pip3 install ipython

To start the IPython command shell, you can simply type ipython3 in the terminal. For more installation instructions, you can visit http://ipython.org/install.html.

pgmpy

pgmpy is a Python library to work with Probabilistic Graphical models. As it's currently not on PyPi, we will need to build it manually. You can get the source code from the Git repository using the following command:

>>> git clone https://github.com/pgmpy/pgmpy

Now cd into the cloned directory switch branch for version used in this book and build it with the following code:

>>> cd pgmpy
>>> git checkout book/v0.1
>>> sudo python3 setup.py install

For more installation instructions, you can visit http://pgmpy.org/install.html.

With both IPython and pgmpy installed, you should now be able to run the examples in the book.

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Tech Concepts
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Programming languages
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