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Key FeaturesBook DescriptionWhat 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 realworld examplesDeploy probabilistic graphical models using various libraries in PythonGain working details of Hidden Markov models with realworld examplesWho 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. |
