# Think Bayes 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.

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491945438

Category: Mathematics

Page: 210

View: 448

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.
Categories: Mathematics

# Think Bayes 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.

Author: Allen Downey

Publisher: O'Reilly Media

ISBN: 149208946X

Category:

Page: 250

View: 518

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
Categories:

# Think Bayes Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Author: Allen B. Downey

Publisher:

ISBN: OCLC:1000360821

Category: Bayesian statistical decision theory

Page: 203

View: 433

Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
Categories: Bayesian statistical decision theory

# Think Bayes Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start.

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 9781492089414

Category: Mathematics

Page: 338

View: 978

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
Categories: Mathematics

# Think Bayes Bayesian Statistics in Python Allen Downey. Math/Statistics/Python. Think. Bayes. 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 solve ...

Author: Allen Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491945445

Category: Computers

Page: 210

View: 687

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.
Categories: Computers

# Think Bayes 2nd Edition Based on undergraduate classes taught by author Allen Downey, this book's computational approach helps you get a solid start.

Author: Allen Downey

Publisher:

ISBN: OCLC:1247846682

Category:

Page: 300

View: 195

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 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 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.
Categories:

# Think Bayes "Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Author: Allen Downey

Publisher:

ISBN: 1491945427

Category: Bayesian statistical decision theory

Page:

View: 321

"Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems."--Open Textbook Library.
Categories: Bayesian statistical decision theory

# Bayesian Analysis with Python If you have general questions about Bayesian statistics, especially those related to PyMC3 or ArviZ, you can ask questions at ... This book does not use PyMC3, but a Python library that's constructed around Think Bayes.

Author: Osvaldo Martin

Publisher: Packt Publishing Ltd

ISBN: 9781789349665

Category: Computers

Page: 356

View: 582

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A 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 learn Build 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 models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who 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.
Categories: Computers

# Python Real World Machine Learning For readers interested in Bayesian statistics, Allen Downey's book, Think Bayes, is a marvelous introduction (and one of my all-time favorite statistics books): https://www.google.co.uk/#q=think+bayes. For readers interested in learning ...

Author: Prateek Joshi

Publisher: Packt Publishing Ltd

ISBN: 9781787120679

Category: Computers

Page: 941

View: 377

Categories: Computers

# Advanced Machine Learning with Python For readers interested in Bayesian statistics, Allen Downey's book, Think Bayes, is a marvelous introduction (and one of my all-time favorite statistics books): https://www.google.co.uk/#q=think+bayes. For readers interested in learning ...

Author: John Hearty

Publisher: Packt Publishing Ltd

ISBN: 9781784393830

Category: Computers

Page: 278

View: 847