Statistical Rethinking PDF Download
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| Author | : Richard McElreath |
| Publisher | : CRC Press |
| Total Pages | : 488 |
| Release | : 2018-01-03 |
| Genre | : Mathematics |
| ISBN | : 1315362619 |
Download Statistical Rethinking Book in PDF, ePub and Kindle
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
| Author | : Andrew Gelman |
| Publisher | : Cambridge University Press |
| Total Pages | : 551 |
| Release | : 2020-07-23 |
| Genre | : Business & Economics |
| ISBN | : 110702398X |
Download Regression and Other Stories Book in PDF, ePub and Kindle
A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
| Author | : Andrew Gelman |
| Publisher | : CRC Press |
| Total Pages | : 663 |
| Release | : 2013-11-27 |
| Genre | : Mathematics |
| ISBN | : 1439898200 |
Download Bayesian Data Analysis Book in PDF, ePub and Kindle
Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, 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. Bayesian Data Analysis, Third Edition continues to take an applied
| Author | : Ben Lambert |
| Publisher | : SAGE |
| Total Pages | : 744 |
| Release | : 2018-04-20 |
| Genre | : Social Science |
| ISBN | : 1526418266 |
Download A Student’s Guide to Bayesian Statistics Book in PDF, ePub and Kindle
Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.
| Author | : Will Kurt |
| Publisher | : No Starch Press |
| Total Pages | : 258 |
| Release | : 2019-07-09 |
| Genre | : Mathematics |
| ISBN | : 1593279566 |
Download Bayesian Statistics the Fun Way Book in PDF, ePub and Kindle
Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.
| Author | : Jean-Michel Marin |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 305 |
| Release | : 2013-10-28 |
| Genre | : Computers |
| ISBN | : 1461486874 |
Download Bayesian Essentials with R Book in PDF, ePub and Kindle
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.
| Author | : Rupert G. Miller, Jr. |
| Publisher | : CRC Press |
| Total Pages | : 340 |
| Release | : 1997-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780412070112 |
Download Beyond ANOVA Book in PDF, ePub and Kindle
Renowned statistician R.G. Miller set the pace for statistics students with Beyond ANOVA: Basics of Applied Statistics. Designed to show students how to work with a set of "real world data," Miller's text goes beyond any specific discipline, and considers a whole variety of techniques from ANOVA to empirical Bayes methods; the jackknife, bootstrap methods; and the James-Stein estimator. This reissue of Miller's classic book has been revised by professors at Stanford University, California. As before, one of the main strengths of Beyond ANOVA is its promotion of the use of the most straightforward data analysis methods-giving students a viable option, instead of resorting to complicated and unnecessary tests. Assuming a basic background in statistics, Beyond ANOVA is written for undergraduates and graduate statistics students. Its approach will also be valued by biologists, social scientists, engineers, and anyone who may wish to handle their own data analysis.
| Author | : Peter D. Hoff |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 271 |
| Release | : 2009-06-02 |
| Genre | : Mathematics |
| ISBN | : 0387924078 |
Download A First Course in Bayesian Statistical Methods Book in PDF, ePub and Kindle
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
| Author | : Craig Calcaterra |
| Publisher | : Arcadia Publishing |
| Total Pages | : 147 |
| Release | : 2022-04-05 |
| Genre | : Sports & Recreation |
| ISBN | : 1953368247 |
Download Rethinking Fandom Book in PDF, ePub and Kindle
A fundamental reevaluation of how to be a sports fan by an acclaimed baseball writer. Sports fandom isn't what it used to be. Owners and executives increasingly count on the blind loyalty of their fans and too often act agai
| Author | : Allen B. Downey |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 137 |
| Release | : 2011-07-01 |
| Genre | : Computers |
| ISBN | : 1449313108 |
Download Think Stats Book in PDF, ePub and Kindle
If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data
