The Bayesian Analysis Toolkit, a software package for data analysis based onBayes' theorem, is introduced. This toolkit takes advantage of Markov ChainMonte Carlo to find the full posterior probability distributions. The tool caneasily be used for parameter estimation, limit setting and error propagation.Model comparison and goodness-of-fit estimation are realized in the packagethrough well-established methods. In addition to a brief description of theBayesian Analysis Toolkit, the use of this tool in searches is described in theexample of Banff Challenge 2a problem 1
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
We describe the development of a new toolkit for data analysis. The analysis package is based on Bay...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
This software tool employs Bayesian inference to calculate the posterior probability of a disease di...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches ...
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide vari...
The Bayesian Analysis Toolkit (BAT) is a C++ library designed to analyze data through the applicatio...
Fitting parameters of interest in an elegant and efficient way via analysis of experimental data is ...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
BAT.jl, the Julia version of the Bayesian Analysis Toolkit, is a software package which is designed ...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
We describe the development of a new toolkit for data analysis. The analysis package is based on Bay...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
This software tool employs Bayesian inference to calculate the posterior probability of a disease di...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches ...
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide vari...
The Bayesian Analysis Toolkit (BAT) is a C++ library designed to analyze data through the applicatio...
Fitting parameters of interest in an elegant and efficient way via analysis of experimental data is ...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
BAT.jl, the Julia version of the Bayesian Analysis Toolkit, is a software package which is designed ...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...