Mastering Probabilistic Graphical Models Using Python
Description:... 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 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 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 AlgorithmsSample algorithms in Graphical ModelsGrasp details of Naive Bayes with real-world examplesDeploy PGMs 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 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 approachAn 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.
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