Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
About This Book
Gain in-depth knowledge of Probabilistic Graphical Models
Model time-series problems using Dynamic Bayesian Networks
A practical guide to help you apply PGMs to real-world problems
Who This Book Is For
If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem.
What You Will Learn
Get to know the basics of Probability theory and Graph Theory
Work with Markov Networks
Implement Bayesian Networks
Exact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm
Understand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms
Sample algorithms in Graphical Models
Grasp details of Naive Bayes with real-world examples
Deploy PGMs using various libraries in Python
Gain working details of Hidden Markov Models with real-world examples
In Detail
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.
Style and approach
An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.
Description:
Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
About This Book
Who This Book Is For
If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem.
What You Will Learn
In Detail
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.
Style and approach
An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.