Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath
Requirements: .ePUB, .PDF reader, 6.8 MB, 14.3 MB
Overview: 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.
Genre: Non-Fiction > Educational
Download Instructions:
ePUB:
(Closed Filehost) (Closed Filehost) https://sendit.cloud/j48kb2hws84a
https://send.firefox.com/download/85406dd2b9d1755e/#c7BQTDB9ba8pObcD74_mpw
PDF:
(Closed Filehost) (Closed Filehost) https://sendit.cloud/x3d2h4ipnilb
https://send.firefox.com/download/86464e97bb1c26fd/#7Qc3Ci7JSOp-PzJA8-cd9Q
Requirements: .ePUB, .PDF reader, 6.8 MB, 14.3 MB
Overview: 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.
Genre: Non-Fiction > Educational
Download Instructions:
ePUB:
(Closed Filehost) (Closed Filehost) https://sendit.cloud/j48kb2hws84a
https://send.firefox.com/download/85406dd2b9d1755e/#c7BQTDB9ba8pObcD74_mpw
PDF:
(Closed Filehost) (Closed Filehost) https://sendit.cloud/x3d2h4ipnilb
https://send.firefox.com/download/86464e97bb1c26fd/#7Qc3Ci7JSOp-PzJA8-cd9Q