Bayesian Analysis with Python: A practical guide to probabilistic modeling, Edition 3
About this ebook
- Conduct Bayesian data analysis with step-by-step guidance
- Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling
- Enhance your learning with best practices through sample problems and practice exercises
- Purchase of the print or Kindle book includes a free PDF eBook.
- Build probabilistic models using PyMC and Bambi
- Analyze and interpret probabilistic models with ArviZ
- Acquire the skills to sanity-check models and modify them if necessary
- Build better models with prior and posterior predictive checks
- Learn the advantages and caveats of hierarchical models
- Compare models and choose between alternative ones
- Interpret results and apply your knowledge to real-world problems
- Explore common models from a unified probabilistic perspective
- Apply the Bayesian framework's flexibility for probabilistic thinking
If you are a student, data scientist, researcher, or 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 scientific libraries like NumPy is expected.
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.
