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About this product
- DescriptionMaster probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book * Gain in-depth kwledge of Probabilistic Graphical Models * Model time-series problems using Dynamic Bayesian Networks * A practical guide to help you apply PGMs to real-world problems 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 kw 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. What You Will Learn * Get to kw 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 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. 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.
- Author BiographyAnkur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions. Abinash Panda is an undergraduate from IIT (BHU), Varanasi, and is currently working as a data scientist. He has been a contributor to open source libraries such as the Shogun machine learning toolbox and pgmpy, which he started writing along with four other members. He spends most of his free time on improving pgmpy and helping new contributors.
- Author(s)Abinash Panda,Ankur Ankan
- PublisherPackt Publishing Limited
- Date of Publication03/08/2015
- SubjectComputer Communications & Networking
- Place of PublicationBirmingham
- Country of PublicationUnited Kingdom
- ImprintPackt Publishing Limited
- Content Noteblack & white illustrations
- Weight490 g
- Width190 mm
- Height235 mm
- Spine15 mm
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