Bayesian analysis with Python introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ
Explore fundamentals of Bayesian inference and applications of Bayesian modeling for probabilistic machine learning. About This Book * Take a practical approach to Bayesian modeling and explore its best practices using PyMC3 * Perform Bayesian analysis for Gaussian and Markov processes * Build gener...
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| Vorheriger Titel: | Martin, Osvaldo Bayesian analysis with Python |
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| Format: | UnknownFormat |
| Sprache: | eng |
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Birmingham, Mumbai
Packt
December 2018
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| Ausgabe: | Second Edition |
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Tag hinzufügen | Zusammenfassung: | Explore fundamentals of Bayesian inference and applications of Bayesian modeling for probabilistic machine learning. About This Book * Take a practical approach to Bayesian modeling and explore its best practices using PyMC3 * Perform Bayesian analysis for Gaussian and Markov processes * Build generalized models to solve challenges in classification and regression Who This Book Is For Bayesian Analysis with Python is for you if you are a data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming. Although you need not have any previous statistical knowledge, some experience in using Python is expected. What You Will Learn * Build probabilistic models using the Python library PyMC3 * Acquire the skills required to sanity check models and modify them * Understand the advantages of hierarchical models * Find out how different models can be used to answer different data analysis questions * Detect faults in models and choose between alternative models * Discover the connections between statistics and machine learning * Think probabilistically and benefit from the flexibility of the Bayesian framework In Detail The second edition of Bayesian Analysis with Python covers the core concepts of Bayesian statistics and demonstrates how to apply them to data science. The book starts with an introduction to Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library. You'll take a practical computational approach over a mathematical one. Once you've got to grips with the basics, you'll understand synthetic and real datasets, which are used to explain the fundamentals of the Bayesian approach, and be introduced to several types of models such as generalized linear models for regression and classification, mixture models, hierarchical models, and the Gaussian process, among others. By the end of the book, you will have thoroughly studied probabilistic modeling and will be able to design and implement your own Bayesian models with PyMC3 for various data science tasks. |
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| Beschreibung: | v, 341 Seiten Illustrationen, Diagramme |
| ISBN: | 1789341655 1-78934-165-5 9781789341652 978-1-78934-165-2 |