Think Bayes Bayesian Statistics in Python

Name: Think Bayes Bayesian Statistics in Python
Price: 9.73 USD
Availability: InStock
ISBN: 9781449370787

ISBN-10: 1449370780

ISBN-13: 9781449370787

Edition: 2013

Authors: Allen B. Downey

List price: $23.99

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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 and Python objects to represent 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 be able to apply these techniques to real-world problems.Use your existing programming skills to learn and understand Bayesian statisticsLearn Bayes’s Theorem, computational statistics, estimation, odds, decision analysis, prediction, observer bias, and hypothesis testingUse for loops in Python rather than complex…

Book details

List price: $23.99
Copyright year: 2013
Publisher: O'Reilly Media, Incorporated
Publication date: 10/15/2013
Binding: Paperback
Pages: 214
Size: 7.00" wide x 9.19" long x 0.41" tall
Weight: 1.012
Language: English

Allen Downey is an Associate Professor of Computer Science at the 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.



Preface



Bayes's Theorem


Conditional probability


Conjoint probability


The cookie problem


Bayes's theorem


The diachronic interpretation


The M&M problem


The Monty Hall problem


Discussion



Computational Statistics


Distributions


The cookie problem


The Bayesian framework


The Monty Hall problem


Encapsulating the framework


The M&M problem


Discussion


Exercises



Estimation


The dice problem


The locomotive problem


What about that prior?


An alternative prior


Credible intervals


Cumulative distribution functions


The German tank problem


Discussion


Exercises



More Estimation


The Euro problem


Summarizing the posterior


Swamping the priors


Optimization


The beta distribution


Discussion


Exercises



Odds and Addends


Odds


The odds form of Bayes's theorem


Oliver's blood


Addends


Maxima


Mixtures


Discussion



Decision Analysis


The Price is Right problem


The prior


Probability density functions


Representing PDFs


Modeling the contestants


Likelihood


Update


Optimal bidding


Discussion



Prediction


The Boston Bruins problem


Poisson processes


The posteriors


The distribution of goals


The probability of winning


Sudden death


Discussion


Exercises



Observer Bias


The Red Line problem


The model


Wait times


Predicting wait times


Estimating the arrival rate


Incorporating uncertainty


Decision analysis


Discussion


Exercises



Two Dimensions


Paintball


The suite


Trigonometry


Likelihood


Joint distributions


Conditional distributions


Credible intervals


Discussion


Exercises



Approximate Bayesian Computation


The Variability Hypothesis


Mean and standard deviation


Update


The posterior distribution of CV


Underflow


Log-likelihood


A little optimization


ABC


Robust estimation


Who is more variable?


Discussion


Exercises



Hypothesis Testing


Back to the Euro problem


Making a fair comparison


The triangle prior


Discussion


Exercises



Evidence


Interpreting SAT scores


The scale


The prior


Posterior


A better model


Calibration


Posterior distribution of efficacy


Predictive distribution


Discussion



Simulation


The Kidney Tumor problem


A simple model


A more general model


Implementation


Caching the joint distribution


Conditional distributions


Serial Correlation


Discussion



A Hierarchical Model


The Geiger counter problem


Start simple


Make it hierarchical


A little optimization


Extracting the posteriors


Discussion


Exercises



Dealing with Dimensions


Belly button bacteria


Lions and tigers and bears


The hierarchical version


Random sampling


Optimization


Collapsing the hierarchy


One more problem


We're not done yet


The belly button data


Predictive distributions


Joint posterior


Coverage


Discussion


Index