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Statistisches Umdenken: Ein Bayes-Kurs mit Beispielen in R und STAN 2. Auflage-

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Statistical Rethinking: A Bayesian Course with Examples in R and STAN 2nd Editio
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ISBN-13
9780367139919
Book Title
Statistical Rethinking
ISBN
9780367139919
Publication Name
Statistical Rethinking : a Bayesian Course with Examples in Rand Stan
Item Length
10.3in
Publisher
CRC Press LLC
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Publication Year
2020
Type
Textbook
Format
Hardcover
Language
English
Item Height
1.4in
Author
Richard Mcelreath
Item Width
7.3in
Item Weight
50.2 Oz
Number of Pages
594 Pages

Über dieses Produkt

Product Information

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses. Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub

Product Identifiers

Publisher
CRC Press LLC
ISBN-10
036713991x
ISBN-13
9780367139919
eBay Product ID (ePID)
26038269102

Product Key Features

Author
Richard Mcelreath
Publication Name
Statistical Rethinking : a Bayesian Course with Examples in Rand Stan
Format
Hardcover
Language
English
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Publication Year
2020
Type
Textbook
Number of Pages
594 Pages

Dimensions

Item Length
10.3in
Item Height
1.4in
Item Width
7.3in
Item Weight
50.2 Oz

Additional Product Features

Lc Classification Number
Qa279.5.M3971 2020
Edition Number
2
Reviews
"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath's engaging writing style and humor, and personally found the infusion of humor quite refreshing." ~Adam Loy, Carleton College "(The chapter) 'Generalized Linear Madness' represents another great chapter of an even better edition of an already awesome textbook." ~Benjamin K. Goodrich, Columbia University "(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory." ~Josep Fortiana Gregori, University of Barcelona "I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." ~Nguyet Nguyen, Youngstown State University
Table of Content
1. The Golem of Prague. 2. Small Worlds and Large Worlds. Chapter 3. Sampling the Imaginary. 4. Geocentric Models. 5. The Many Variables & The Spurious Waffles. 6. The Haunted DAG & The Causal Terror. 7. Ulysses' Compass. 8. Conditional Manatees. 8. Conditional Manatees. 9. Markov Chain Monte Carlo. 10. Big Entropy and the Generalized Linear Model. 11. God Spiked the Integers. 12. Monsters and Mixtures. 13. Models With Memory. 14. Adventures in Covariance. 15. Missing Data and Other Opportunities. 16. Generalized Linear Madness. 17. Horoscopes.
Copyright Date
2020
Topic
Life Sciences / Biological Diversity, Probability & Statistics / General, General
Lccn
2019-957006
Intended Audience
College Audience
Illustrated
Yes
Genre
Science, Mathematics

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