We learned that linear regression is one of the most widely used models in statistics and machine learning and it is also the building block of several more complex methods. This is a widely used model and different people tend to give different names to the same concept or object. Thus, we first introduced some commonly used vocabulary in statistics and machine learning. We studied the core of the linear model, an expression to connect an input variable to an output variable. In this chapter, we performed that connection using Gaussian and Student's t-distributions and in future chapters we will extend this model to other distributions. We dealt with computational problems and how to fix them by centering and/or standardizing the data and we had the opportunity to clearly see the advantages of using NUTS over Metropolis sampler. We adapted the hierarchical model introduced in the past chapter to simple linear regression. We also explored polynomial regression to fit curved lines...
Bayesian Analysis with Python
Bayesian Analysis with Python
Overview of this book
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.
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Bayesian Analysis with Python
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Table of Contents (15 chapters)
Bayesian Analysis with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Thinking Probabilistically - A Bayesian Inference Primer
Programming Probabilistically – A PyMC3 Primer
Juggling with Multi-Parametric and Hierarchical Models
Understanding and Predicting Data with Linear Regression Models
Classifying Outcomes with Logistic Regression
Model Comparison
Mixture Models
Gaussian Processes
Index
Customer Reviews