Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, Edition 2
About this ebook
- A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ
- A modern, practical and computational approach to Bayesian statistical modeling
- A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.
- Build probabilistic models using the Python library PyMC3
- Analyze probabilistic models with the help of ArviZ
- Acquire the skills required to sanity check models and modify them if necessary
- Understand the advantages and caveats of hierarchical models
- Find out how different models can be used to answer different data analysis questions
- Compare models and choose between alternative ones
- Discover how different models are unified from a probabilistic perspective
- Think probabilistically and benefit from the flexibility of the Bayesian framework
If 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.
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About the author
Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
