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Think Bayes by Allen Downey

Title Think Bayes
Author Allen Downey
Publisher "O'Reilly Media, Inc."
Release 2013-09-12
Category Computers
Total Pages 213
ISBN 1491945443
Language English, Spanish, and French
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Book Summary:

If you know how to program with Python, and know a little about probability, you're ready to tackle Bayesian statistics. This book shows you how to use Python code instead of math to help you learn Bayesian fundamentals. Once you get the math out of the way, you'll be able to apply these techniques to real-world problems.

Think Bayes by Allen B. Downey

Title Think Bayes
Author Allen B. Downey
Publisher "O'Reilly Media, Inc."
Release 2021-05-18
Category Computers
Total Pages 338
ISBN 1492089435
Language English, Spanish, and French
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Book Summary:

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems

Bayesian Analysis with Python by Osvaldo Martin

Title Bayesian Analysis with Python
Author Osvaldo Martin
Publisher Packt Publishing Ltd
Release 2018-12-26
Category Computers
Total Pages 356
ISBN 1789349664
Language English, Spanish, and French
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Book Summary:

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key FeaturesA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZA modern, practical and computational approach to Bayesian statistical modelingA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learnBuild probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical modelsFind out how different models can be used to answer different data analysis questionsCompare models and choose between alternative onesDiscover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian frameworkWho this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

Think Bayes by Allen B. Downey

Title Think Bayes
Author Allen B. Downey
Publisher "O'Reilly Media, Inc."
Release 2013-09-12
Category Mathematics
Total Pages 277
ISBN 1491945435
Language English, Spanish, and French
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Book Summary:

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

Think Bayes by Allen Downey

Title Think Bayes
Author Allen Downey
Publisher O'Reilly Media
Release 2021-09-14
Category
Total Pages 250
ISBN 9781492089469
Language English, Spanish, and French
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Book Summary:

If you know how to program with Python, youâ??re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youâ??ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this bookâ??s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems

Title Perception as Bayesian Inference
Author David C. Knill
Publisher Cambridge University Press
Release 1996-09-13
Category Computers
Total Pages 538
ISBN 9780521461092
Language English, Spanish, and French
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Book Summary:

This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.

Title The English Reports
Author
Publisher
Release 1913
Category Law reports, digests, etc
Total Pages
ISBN
Language English, Spanish, and French
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Book Summary:

Title The English Reports
Author
Publisher
Release 1905
Category Law reports, digests, etc
Total Pages 1234
ISBN
Language English, Spanish, and French
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Book Summary:

Think Bayes by Allen B. Downey

Title Think Bayes
Author Allen B. Downey
Publisher "O'Reilly Media, Inc."
Release 2021-05-18
Category Mathematics
Total Pages 338
ISBN 1492089419
Language English, Spanish, and French
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Book Summary:

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems