{"product_id":"9781789341652","title":"Bayesian Analysis with Python - Second Edition: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ","description":"\u003cp\u003e\u003cstrong\u003eBayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ \u003c\/li\u003e \u003cli\u003eA modern, practical and computational approach to Bayesian statistical modeling \u003c\/li\u003e \u003cli\u003eA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eThe second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. \u003c\/p\u003e \u003cp\u003eThe main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. \u003c\/p\u003e \u003cp\u003eBy the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat you will learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eBuild probabilistic models using the Python library PyMC3 \u003c\/li\u003e \u003cli\u003eAnalyze probabilistic models with the help of ArviZ \u003c\/li\u003e \u003cli\u003eAcquire the skills required to sanity check models and modify them if necessary \u003c\/li\u003e \u003cli\u003eUnderstand the advantages and caveats of hierarchical models \u003c\/li\u003e \u003cli\u003eFind out how different models can be used to answer different data analysis questions \u003c\/li\u003e \u003cli\u003eCompare models and choose between alternative ones \u003c\/li\u003e \u003cli\u003eDiscover how different models are unified from a probabilistic perspective \u003c\/li\u003e \u003cli\u003eThink probabilistically and benefit from the flexibility of the Bayesian framework\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eIf you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.\u003c\/p\u003e","brand":"Packt Publishing","offers":[{"title":"Default Title","offer_id":46399314428145,"sku":"9781789341652","price":50.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0674\/5433\/7265\/files\/9781789341652_p0.jpg?v=1765269584","url":"https:\/\/shop.barnesandnoble.com\/products\/9781789341652","provider":"Barnes \u0026 Noble","version":"1.0","type":"link"}