There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The ear
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The exi...
This is a collection of practical exercises from old courses, mostly my old module in “Bayesian Comp...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
There has been dramatic growth in the development and application of Bayesian inference in statistic...
International audienceThis Bayesian modeling book provides a self-contained entry to computational B...
There is an explosion of interest in Bayesian statistics, primarily because recently created computa...
Background: Many recent statistical applications involve inference under complex models, where it is...
Bayesian methodology differs from traditional statistical methodology which involves frequentist app...
Color poster with text and graphs.Conventional frequentist statistics taught in undergraduate course...
A fully Bayesian computing environment calls for the possibility of defining vector and array object...
Computational techniques based on simulation have now become an essential part of the statistician's...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a se...
Engaging and accessible, this book teaches readers how to use inferential statistical thinking to ch...
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The exi...
This is a collection of practical exercises from old courses, mostly my old module in “Bayesian Comp...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
There has been dramatic growth in the development and application of Bayesian inference in statistic...
International audienceThis Bayesian modeling book provides a self-contained entry to computational B...
There is an explosion of interest in Bayesian statistics, primarily because recently created computa...
Background: Many recent statistical applications involve inference under complex models, where it is...
Bayesian methodology differs from traditional statistical methodology which involves frequentist app...
Color poster with text and graphs.Conventional frequentist statistics taught in undergraduate course...
A fully Bayesian computing environment calls for the possibility of defining vector and array object...
Computational techniques based on simulation have now become an essential part of the statistician's...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a se...
Engaging and accessible, this book teaches readers how to use inferential statistical thinking to ch...
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The exi...
This is a collection of practical exercises from old courses, mostly my old module in “Bayesian Comp...