Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)

Name: Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)
Price: 66.86 USD
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Description

Book Synopsis: 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.

Details

Tired of struggling with complex statistical concepts and unsure of how to make inferences from data? Look no further! The Statistical Rethinking book is here to help. This comprehensive Bayesian course, with examples in R and Stan, will not only build your knowledge but also boost your confidence in understanding and interpreting data.

One of the unique features of this book is its emphasis on step-by-step calculations, which are usually automated. By performing these calculations yourself, you gain a deeper understanding of the underlying principles and are able to make informed choices in your own modeling work. Say goodbye to relying solely on automated tools and hello to being in full control of your statistical analyses.

But that's not all - the second edition of Statistical Rethinking takes it a step further by introducing the directed acyclic graph (DAG) approach to causal inference. Through the integration of DAGs into numerous examples, you'll learn a powerful technique for understanding and analyzing causal relationships in your data.

Still not convinced? The book also covers a wide range of topics, including regression, multilevel models, measurement error, missing data, and much more. With detailed explanations of the underlying mathematics and practical examples using the latest R packages, this book is your one-stop resource for all things Bayesian.

Ready to take your statistical analysis skills to the next level? Don't miss out on this opportunity to enhance your understanding of modeling and gain confidence in making accurate inferences. Order your copy of Statistical Rethinking: A Bayesian Course with Examples in R and Stan today!

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