Bayesian statistics is an effective tool for solving some inference problems when the available sample is too small for more complex statistical analysis to be applied. This course teaches the main concepts of Bayesian data analysis. It focuses on how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, model checking, and validation.
Introduction to Bayesian Analysis in Python [Video] by Packt will teach you the core concepts of Bayesian data analysis. It will help you learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, model checking, and validation.
What You Will Learn:
- 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
- Implement parametric models for your generalized linear models
- 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
- Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework
All the codes of the course are uploaded on the Github repository: https://github.com/PacktPublishing/-Introduction-to-Bayesian-Analysis-in-Python
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