- 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.
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
- 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
Style and approachBayes algorithms are widely used in statistics, machine learning, artificial intelligence, and data mining. This will be a practical guide allowing the readers to use Bayesian methods for statistical modelling and analysis using Python.
Title: Bayesian Analysis with Python
By: Osvaldo Martin
Ebook: November 2016
Pages: 282
Ebook ISBN: 978-1-78588-985-1 | ISBN 10: 1-78588-985-0