Even to the initiated, statistical calculations based on Bayes's Theorem can be daunting because of the nu-merical integrations required in all but the simplest ap-plications. Moreover, from a teaching perspective, in-troductions to Bayesian statistics-if they are given at all-are circumscribed by these apparent calculational difficulties. Here we offer a straightforward sampling-resampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily imple-mented calculation strategies
Bayesian methods are genuine inferences which admit genuine confidence intervals and more truly refl...
Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear...
There is a current emphasis on making the introductory statistics class more dataoriented. Data dist...
This paper explains why it is important to understand Bayesian techniques and how they are advantage...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This paper explores the why and what of statistical learning from a computational modelling perspect...
. This paper is an attempt to reconcile Bayesian and non-Bayesian approaches to statistical inferenc...
Scientific learning is seen as an iterative process employing Criticism and Estimation. Sampling the...
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
The methods of teaching statistical inference vary and too often, insufficient links are made to the...
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular a...
University courses in elementary statistics are usually taught from a frequentist perspective. In th...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
"Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowle...
Bayesian methods are genuine inferences which admit genuine confidence intervals and more truly refl...
Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear...
There is a current emphasis on making the introductory statistics class more dataoriented. Data dist...
This paper explains why it is important to understand Bayesian techniques and how they are advantage...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This paper explores the why and what of statistical learning from a computational modelling perspect...
. This paper is an attempt to reconcile Bayesian and non-Bayesian approaches to statistical inferenc...
Scientific learning is seen as an iterative process employing Criticism and Estimation. Sampling the...
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
The methods of teaching statistical inference vary and too often, insufficient links are made to the...
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular a...
University courses in elementary statistics are usually taught from a frequentist perspective. In th...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
"Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowle...
Bayesian methods are genuine inferences which admit genuine confidence intervals and more truly refl...
Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear...
There is a current emphasis on making the introductory statistics class more dataoriented. Data dist...