Think bayes bayesian statistics in python

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 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, 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.

Allen Downey is a Professor of Computer Science at Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master’s and Bachelor’s degrees from MIT. He is the author of Think Python, Think Bayes, Think DSP, and a blog, Probably Overthinking It.

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O’Reilly® Think Bayes: Bayesian Statistics in Python (Allen B. Downey)
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Think Stats, 2nd Edition: Exploratory Data Analysis in Python This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You’ll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

Bayesian Methods for Hackers: Probabilistic Programming This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation — no advanced mathematics required.

Bayesian Methods for Statistical Analysis (Borek Puza) Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

Bayesian Reasoning and Machine Learning (David Barber) This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You’ll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

An Introduction to Bayesian Thinking (Merlise Clyde, et al.) This book provides an introduction to Bayesian inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R for anyone interested in learning about Bayesian statistics.

Bayesian Data Analysis (Andrew Gelman, et al.) This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.

Bayesian Networks and BayesiaLab (Stefan Conrady, et al.) This practical introduction is geared towards scientists who wish to employ Bayesian Networks for applied research using the BayesiaLab software platform. It can serve as a self-study guide for learners and as a reference manual for advanced practitioners.

Источник

Аллен Дауни: Байесовские модели. Байесовская статистика на языке Python

Курьер 0 ₽ 35 %

Аннотация к книге «Байесовские модели. Байесовская статистика на языке Python»

Если вы знаете, как программировать на Python и немного знаете о теории вероятности, значит, вы готовы освоить байесовскую статистику. Эта книга расскажет вам, как решать статистические задачи с помощью языка Python вместо математических формул и использовать дискретные вероятностные распределения вместо непрерывной математики. Когда вы уберете с дороги математику, байесовские основы станут яснее, и вы начнете применять эту технику для решения реальных проблем.
Байесовские статистические методы становятся все более обширными и важными. Но в помощь начинающим доступно не слишком много источников. Изложенная в этой книге методика основана на материале проводимых автором студенческих занятиях и точно поможет вам сделать хороший старт!
Издание будет полезно всем специалистам по анализу данных, кто должен использовать статистические данные в условиях их неполноты или решать другие нетривиальные задачи, связанные с вероятностными распределениями.

Иллюстрации к книге Аллен Дауни — Байесовские модели. Байесовская статистика на языке Python

Вы можете стать одним из первых, кто напишет рецензию на эту книгу, и получить бонус — до 15 рублей на баланс в Лабиринте! Или оставьте заявку, чтобы кто-то другой написал ее скорее.

В первой главе обнаружил семь грубых ошибок. Во второй — одну. Похоже придется переходить на оригинал (Think Bayes распространяется свободно).

Не слишком объёмная книга, описывающая применение несложных классов Питон для решения ряда статистических задач, включая такие экзотические как оценка количества бактерий в пупках людей. Для вдумчивого освоения темы нужно конечно прогонять — крутить прилагаемые примеры, на что у меня времени и возможности при моей манере чтения нет, так что кучу сложных и непонятных моментов я попросту пропустил до того момента как подойдёт время. Но понимание того, как используется Байесовское оценивание, мне.

Источник

Think Bayes 2e

Read Think Bayes 2e online (and follow the links there to the Jupyter notebooks).

The code for this book is in this GitHub repository.

This page is for the second edition of Think Bayes. The first edition is still available here.

Description

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 functions. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are loops or array operations.

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.

What’s new in the second edition?

I wrote a new Chapter 1 that introduces conditional probability using the Linda the Banker problem and data from the General Social Survey.
I added new chapters on survival analysis, linear regression, logistic regression, conjugate priors, MCMC, and ABC.
I added a lot of new examples and exercises, most from classes I taught using the first edition.
I rewrote all of the code using NumPy, SciPy, and Pandas (rather than basic Python types). The new code is shorter, clearer, and faster!
For every chapter, there’s a Jupyter notebook where you can read the text, run the code, and work on exercises. You can run the notebooks on your own computer or, if you don’t want to install anything, you can run them on Colab.

More generally, the second edition reflects everything I’ve learned in the 10 years since I started the first edition, and it benefits from the comments, suggestions, and corrections I’ve received from readers.

Free books

Think Bayes is a Free Book, which means that you are free to copy, distribute, and modify it, as long as you attribute the work, share alike, and don’t use it for commercial purposes.

Other Free Books by Allen Downey are available from Green Tea Press.

Источник

Think Bayes

Read it now on the O’Reilly learning platform with a 10-day free trial.

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Book description

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.

Publisher resources

Preface
1. My theory, which is mine
2. Modeling and approximation
3. Working with the code
4. Code style
5. Prerequisites
6. Conventions Used in This Book
7. Safari® Books Online
8. How to Contact Us
9. Contributor List
1. Conditional probability
2. Conjoint probability
3. The cookie problem
4. Bayes’s theorem
5. The diachronic interpretation
6. The M&M problem
7. The Monty Hall problem
8. Discussion
1. Distributions
2. The cookie problem
3. The Bayesian framework
4. The Monty Hall problem
5. Encapsulating the framework
6. The M&M problem
7. Discussion
8. Exercises
1. The dice problem
2. The locomotive problem
3. What about that prior?
4. An alternative prior
5. Credible intervals
6. Cumulative distribution functions
7. The German tank problem
8. Discussion
9. Exercises
1. The Euro problem
2. Summarizing the posterior
3. Swamping the priors
4. Optimization
5. The beta distribution
6. Discussion
7. Exercises
1. Odds
2. The odds form of Bayes’s theorem
3. Oliver’s blood
4. Addends
5. Maxima
6. Mixtures
7. Discussion
1. The Price is Right problem
2. The prior
3. Probability density functions
4. Representing PDFs
5. Modeling the contestants
6. Likelihood
7. Update
8. Optimal bidding
9. Discussion
1. The Boston Bruins problem
2. Poisson processes
3. The posteriors
4. The distribution of goals
5. The probability of winning
6. Sudden death
7. Discussion
8. Exercises
1. The Red Line problem
2. The model
3. Wait times
4. Predicting wait times
5. Estimating the arrival rate
6. Incorporating uncertainty
7. Decision analysis
8. Discussion
9. Exercises
1. Paintball
2. The suite
3. Trigonometry
4. Likelihood
5. Joint distributions
6. Conditional distributions
7. Credible intervals
8. Discussion
9. Exercises
1. The Variability Hypothesis
2. Mean and standard deviation
3. Update
4. The posterior distribution of CV
5. Underflow
6. Log-likelihood
7. A little optimization
8. ABC
9. Robust estimation
10. Who is more variable?
11. Discussion
12. Exercises
1. Back to the Euro problem
2. Making a fair comparison
3. The triangle prior
4. Discussion
5. Exercises
1. Interpreting SAT scores
2. The scale
3. The prior
4. Posterior
5. A better model
6. Calibration
7. Posterior distribution of efficacy
8. Predictive distribution
9. Discussion
1. The Kidney Tumor problem
2. A simple model
3. A more general model
4. Implementation
5. Caching the joint distribution
6. Conditional distributions
7. Serial Correlation
8. Discussion
1. The Geiger counter problem
2. Start simple
3. Make it hierarchical
4. A little optimization
5. Extracting the posteriors
6. Discussion
7. Exercises
1. Belly button bacteria
2. Lions and tigers and bears
3. The hierarchical version
4. Random sampling
5. Optimization
6. Collapsing the hierarchy
7. One more problem
8. We’re not done yet
9. The belly button data
10. Predictive distributions
11. Joint posterior
12. Coverage
13. Discussion
Product information
- Title: Think Bayes
- Author(s): Allen B. Downey
- Release date: September 2013
- Publisher(s): O’Reilly Media, Inc.
- ISBN: 9781449370787
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Think bayes bayesian statistics in python