Non-parametric statistics is often described as the set of statistical tools/models that do not rely on parameterized families of probability distributions. From this definition, it may sound as if Bayesian non-parametric is not possible since we have learned that the first step in doing Bayesian statistics is precisely combining probability distributions in a full probabilistic model. We said in Chapter 1, Thinking Probabilistically - A Bayesian Inference Primer, that probability distributions are the building blocks of probabilistic models. Under the Bayesian paradigm, non-parametric models refer to models with an infinite number of parameters. So, we will define parametric models as those models for which the number of parameters is allowed to grow with the size of the data. For these models, the theoretical numbers of parameters is infinite and we use the data to collapse it to a finite number, thus we allow the data to effectively determine the number of parameters...
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