All models that we have seen so far were parametric models. These are models with a fixed number of parameters that we are interested in estimating. Another type of models are those known as non-parametric models. Non-parametric models are models where the number of parameters increases with the data, in other words, models with a potentially infinite number of parameters that we somehow manage to reduce to a finite number, just those necessary to describe the data. We will began the chapter, by learning about the concept of a kernel, and how to rethink problems in terms of kernels. Gaussians are the workhorse of statistics and this is not only true for classical methods, but also Bayesian statistics and machine learning. We are going to see a clear example of this as we explore how to extend the notion of Gaussian distribution to infinitely large dimensions and how to learn distributions over functions. Even though this will seem really weird at first, it will...
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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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