Bayesian Analysis with Python

Unleash the power and flexibility of the Bayesian framework

Bayesian Analysis with Python

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Osvaldo Martin

3 customer reviews
Unleash the power and flexibility of the Bayesian framework
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Book Details

ISBN 139781785883804
Paperback282 pages

Book Description

The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.

Table of Contents

Chapter 1: Thinking Probabilistically - A Bayesian Inference Primer
Statistics as a form of modeling
Probabilities and uncertainty
Single parameter inference
Posterior predictive checks
Installing the necessary Python packages
Summary
Exercises
Chapter 2: Programming Probabilistically – A PyMC3 Primer
Probabilistic programming
PyMC3 introduction
Summarizing the posterior
Summary
Keep reading
Exercises
Chapter 3: Juggling with Multi-Parametric and Hierarchical Models
Nuisance parameters and marginalized distributions
Gaussians, Gaussians, Gaussians everywhere
Comparing groups
Hierarchical models
Summary
Keep reading
Exercises
Chapter 4: Understanding and Predicting Data with Linear Regression Models
Simple linear regression
Robust linear regression
Hierarchical linear regression
Polynomial regression
Multiple linear regression
The GLM module
Summary
Keep reading
Exercises
Chapter 5: Classifying Outcomes with Logistic Regression
Logistic regression
Multiple logistic regression
Discriminative and generative models
Summary
Keep reading
Exercises
Chapter 6: Model Comparison
Occam's razor – simplicity and accuracy
Regularizing priors
Predictive accuracy measures
Bayes factors
Bayes factors and information criteria
Summary
Keep reading
Exercises
Chapter 7: Mixture Models
Mixture models
Model-based clustering
Continuous mixtures
Summary
Keep reading
Exercises
Chapter 8: Gaussian Processes
Non-parametric statistics
Kernel-based models
Gaussian processes
Summary
Keep reading
Exercises

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

Authors

Table of Contents

Chapter 1: Thinking Probabilistically - A Bayesian Inference Primer
Statistics as a form of modeling
Probabilities and uncertainty
Single parameter inference
Posterior predictive checks
Installing the necessary Python packages
Summary
Exercises
Chapter 2: Programming Probabilistically – A PyMC3 Primer
Probabilistic programming
PyMC3 introduction
Summarizing the posterior
Summary
Keep reading
Exercises
Chapter 3: Juggling with Multi-Parametric and Hierarchical Models
Nuisance parameters and marginalized distributions
Gaussians, Gaussians, Gaussians everywhere
Comparing groups
Hierarchical models
Summary
Keep reading
Exercises
Chapter 4: Understanding and Predicting Data with Linear Regression Models
Simple linear regression
Robust linear regression
Hierarchical linear regression
Polynomial regression
Multiple linear regression
The GLM module
Summary
Keep reading
Exercises
Chapter 5: Classifying Outcomes with Logistic Regression
Logistic regression
Multiple logistic regression
Discriminative and generative models
Summary
Keep reading
Exercises
Chapter 6: Model Comparison
Occam's razor – simplicity and accuracy
Regularizing priors
Predictive accuracy measures
Bayes factors
Bayes factors and information criteria
Summary
Keep reading
Exercises
Chapter 7: Mixture Models
Mixture models
Model-based clustering
Continuous mixtures
Summary
Keep reading
Exercises
Chapter 8: Gaussian Processes
Non-parametric statistics
Kernel-based models
Gaussian processes
Summary
Keep reading
Exercises

Book Details

ISBN 139781785883804
Paperback282 pages
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