Osvaldo Martin's Bayesian Analysis with Python PDF

By Osvaldo Martin

Key Features

  • Simplify the Bayes approach for fixing complicated statistical difficulties utilizing Python;
  • Tutorial advisor that might take the you thru the adventure of Bayesian research with the aid of pattern difficulties and perform exercises;
  • Learn how and whilst to exploit Bayesian research on your functions with this guide.

Book Description

The goal of this ebook is to coach the most options of Bayesian info research. we are going to easy methods to successfully use PyMC3, a Python library for probabilistic programming, to accomplish Bayesian parameter estimation, to examine versions and validate them. This booklet starts off offering the foremost suggestions of the Bayesian framework and the most benefits of this procedure from a realistic perspective. relocating on, we'll discover the ability and adaptability of generalized linear versions and the way to conform them to a wide range of difficulties, together with regression and type. we'll additionally check out blend versions and clustering facts, and we'll end with complicated themes like non-parametrics versions and Gaussian techniques. With assistance from Python and PyMC3 you are going to discover ways to enforce, fee and extend Bayesian versions to unravel information research problems.

What you are going to learn

  • Understand the necessities Bayesian suggestions from a pragmatic element of view
  • Learn how you can construct probabilistic versions utilizing the Python library PyMC3
  • Acquire the talents to sanity-check your versions and alter 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 assorted info research questions
  • When doubtful, discover ways to choose from replacement models.
  • Predict non-stop goal results utilizing regression research or assign sessions utilizing logistic and softmax regression.
  • Learn easy methods to imagine probabilistically and unharness the facility 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 answerable for the merchandising of technology and expertise in Argentina. He has labored on structural bioinformatics and computational biology difficulties, specially on tips on how to validate structural protein types. He has event in utilizing Markov Chain Monte Carlo how you can simulate molecules and likes to use Python to resolve info research difficulties. He has taught classes approximately structural bioinformatics, Python programming, and, extra lately, Bayesian facts research. Python and Bayesian facts have reworked the way in which he appears at technology and thinks approximately difficulties usually. Osvaldo was once fairly prompted to put in writing this ebook to aid others in constructing probabilistic types with Python, despite their mathematical heritage. he's an energetic 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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Bayesian Analysis with Python by Osvaldo Martin

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