Bayesian Analysis with Python - download pdf or read online

By Osvaldo Martin

Key Features

  • Simplify the Bayes procedure for fixing complicated statistical difficulties utilizing Python;
  • Tutorial consultant that would take the you thru the adventure of Bayesian research with the aid of pattern difficulties and perform exercises;
  • Learn how and while to take advantage of Bayesian research on your purposes with this guide.

Book Description

The function of this publication is to coach the most innovations of Bayesian information research. we'll how one can successfully use PyMC3, a Python library for probabilistic programming, to accomplish Bayesian parameter estimation, to examine types and validate them. This booklet starts providing the most important ideas of the Bayesian framework and the most merits of this process from a realistic viewpoint. relocating on, we are going to discover the facility and adaptability of generalized linear versions and the way to conform them to a big selection of difficulties, together with regression and category. we'll additionally inspect mix types and clustering facts, and we are going to end with complex issues like non-parametrics versions and Gaussian methods. With the aid of Python and PyMC3 you are going to learn how to enforce, payment and extend Bayesian versions to resolve information research problems.

What you'll learn

  • Understand the necessities Bayesian thoughts from a realistic aspect of view
  • Learn the way to construct probabilistic types utilizing the Python library PyMC3
  • Acquire the abilities to sanity-check your versions and regulate them if necessary
  • Add constitution for your versions and get the benefits of hierarchical models
  • Find out how assorted types can be utilized to respond to diversified info research questions
  • When doubtful, learn how to choose from replacement models.
  • Predict non-stop objective results utilizing regression research or assign periods utilizing logistic and softmax regression.
  • Learn find out how to imagine probabilistically and unharness the ability and adaptability of the Bayesian framework

About the Author

Osvaldo Martin is a researcher on the nationwide clinical and Technical examine Council (CONICET), the most association in control of the advertising of technological know-how and know-how in Argentina. He has labored on structural bioinformatics and computational biology difficulties, specifically on the right way to validate structural protein versions. He has adventure in utilizing Markov Chain Monte Carlo tips on how to simulate molecules and likes to use Python to unravel info research difficulties. He has taught classes approximately structural bioinformatics, Python programming, and, extra lately, Bayesian information research. Python and Bayesian records have reworked the way in which he appears at technological know-how and thinks approximately difficulties more often than not. Osvaldo was once particularly influenced to write down this publication to aid others in constructing probabilistic types with Python, despite their mathematical history. he's an lively member of the PyMOL group (a C/Python-based molecular viewer), and lately he has been making small contributions to the probabilistic programming library PyMC3.

Table of Contents

  1. Thinking Probabilistically - A Bayesian Inference Primer
  2. Programming Probabilistically – A PyMC3 Primer
  3. Juggling with Multi-Parametric and Hierarchical Models
  4. Understanding and Predicting information with Linear Regression Models
  5. Classifying results with Logistic Regression
  6. Model Comparison
  7. Mixture Models
  8. Gaussian Processes

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