Mastering Probabilistic Graphical Models using Python

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Mastering Probabilistic Graphical Models using Python

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內容描述

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This BookGain in-depth knowledge of Probabilistic Graphical ModelsModel time-series problems using Dynamic Bayesian NetworksA practical guide to help you apply PGMs to real-world problemsWho This Book Is ForIf 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 them in your data science problems.What You Will LearnGet to know the basics of probability theory and graph theoryWork with Markov networksImplement Bayesian networksExact inference techniques in graphical models such as the variable elimination algorithmUnderstand approximate inference techniques in graphical models such as message passing algorithmsSampling algorithms in graphical modelsGrasp details of Naive Bayes with real-world examplesDeploy probabilistic graphical models using various libraries in PythonGain working details of Hidden Markov models with real-world examplesIn DetailProbabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems.Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks.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 run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.