Bayesian Analysis with Python by Osvaldo Martin PDF

By Osvaldo Martin

ISBN-10: 1785883801

ISBN-13: 9781785883804

Key Features

  • Simplify the Bayes approach for fixing advanced statistical difficulties utilizing Python;
  • Tutorial advisor that may 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 ebook is to educate the most innovations of Bayesian info research. we are going to the right way to successfully use PyMC3, a Python library for probabilistic programming, to accomplish Bayesian parameter estimation, to envision versions and validate them. This ebook starts featuring the major suggestions of the Bayesian framework and the most benefits of this strategy from a pragmatic viewpoint. relocating on, we'll discover the facility and adaptability of generalized linear types and the way to evolve them to a wide range of difficulties, together with regression and type. we'll additionally inspect blend versions and clustering info, and we are going to end with complex subject matters like non-parametrics types and Gaussian procedures. With the aid of Python and PyMC3 you are going to discover ways to enforce, fee and extend Bayesian versions to resolve information research problems.

What you are going to learn

  • Understand the necessities Bayesian innovations from a pragmatic element of view
  • Learn easy methods to construct probabilistic versions utilizing the Python library PyMC3
  • Acquire the talents to sanity-check your versions and adjust them if necessary
  • Add constitution for your versions and get some great benefits of hierarchical models
  • Find out how diversified versions can be utilized to reply to assorted info research questions
  • When unsure, learn how to choose from substitute models.
  • Predict non-stop aim results utilizing regression research or assign periods utilizing logistic and softmax regression.
  • Learn the way to imagine probabilistically and unharness the ability and adaptability of the Bayesian framework

About the Author

Osvaldo Martin is a researcher on the nationwide medical and Technical study Council (CONICET), the most association accountable for the promoting of technological know-how and know-how in Argentina. He has labored on structural bioinformatics and computational biology difficulties, particularly on tips on how to validate structural protein types. He has adventure in utilizing Markov Chain Monte Carlo the way to simulate molecules and likes to use Python to resolve facts research difficulties. He has taught classes approximately structural bioinformatics, Python programming, and, extra lately, Bayesian information research. Python and Bayesian information have remodeled the way in which he appears to be like at technological know-how and thinks approximately difficulties normally. Osvaldo used to be particularly encouraged to jot down this ebook to aid others in constructing probabilistic types with Python, despite their mathematical heritage. 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 info with Linear Regression Models
  5. Classifying results with Logistic Regression
  6. Model Comparison
  7. Mixture Models
  8. Gaussian Processes

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