Bayesian Analysis with Python: Unleash the power and flexibility of the Bayesian framework
About this ebook
- Simplify the Bayes process for solving complex statistical problems using Python;
- Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises;
- Learn how and when to use Bayesian analysis in your applications with this guide.
- Understand the essentials Bayesian concepts from a practical point of view
- Learn how to build probabilistic models using the Python library PyMC3
- Acquire the skills to sanity-check your models and modify them if necessary
- Add structure to your models and get the advantages of hierarchical models
- Find out how different models can be used to answer different data analysis questions
- When in doubt, learn to choose between alternative models.
- Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression.
- Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework
Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed.
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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.
