Mastering Probabilistic Graphical Models Using Python

    Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python



    Bookstore > Books > Mastering Probabilistic Graphical Models Using Python

    Price$44.99 - $56.16
    Rating
    AuthorsAnkur Ankan, Abinash Panda
    PublisherPackt Publishing
    Published2015
    Pages284
    LanguageEnglish
    FormatPaper book / ebook (PDF)
    ISBN-101784394688
    ISBN-139781784394684
    EBook Hardcover Paperback

    Probabilistic 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.


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