Bayesian Analysis with Python
With this book, you’ll explore Bayesian data analysis, covering the essential concepts, creating probabilistic models using the Python library PyMC3, understanding how different models can be used to answer different data analysis questions, and more. You’ll gain hands-on knowledge as you work through sample problems and practice exercises.
Offered by
Difficulty Level
Intermediate
Completion Time
9h24m
Language
English
About Book
Who Is This Book For?
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.
Bayesian Analysis with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 9h24m total length
Thinking probabilistically, a Bayesian inference primer
Probabilistic programming, a PyMC3 primer
Learning to juggle with more than one parameter - Multiparametric and Hierarchical models
Understanding and predicting data with linear regression models
Classifying outcomes with logistic regression
Too many or too few parameters? Finding the right number the Bayesian way
Clustering and learning about subgroups in you data with mixture models
Infinitely large models, decision analysis and optimization
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