We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution. Parameter estimation, limit setting and uncertainty propagation are implemented in a straightforward manner. A goodness-of-fit criterion is presented which is intuitive and of great practical use
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
We describe the development of a new toolkit for data analysis. The analysis package is based on Bay...
The Bayesian Analysis Toolkit, a software package for data analysis based onBayes' theorem, is intro...
BAT.jl, the Julia version of the Bayesian Analysis Toolkit, is a software package which is designed ...
The Bayesian Analysis Toolkit (BAT) is a C++ library designed to analyze data through the applicatio...
In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Fitting parameters of interest in an elegant and efficient way via analysis of experimental data is ...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
Bayesian paradigm offers a conceptually simple and coherent system of statistical inference based on...
Markov chain Monte Carlo (MCMC) is the most widely used method of estimating joint posterior distrib...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
We describe the development of a new toolkit for data analysis. The analysis package is based on Bay...
The Bayesian Analysis Toolkit, a software package for data analysis based onBayes' theorem, is intro...
BAT.jl, the Julia version of the Bayesian Analysis Toolkit, is a software package which is designed ...
The Bayesian Analysis Toolkit (BAT) is a C++ library designed to analyze data through the applicatio...
In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Fitting parameters of interest in an elegant and efficient way via analysis of experimental data is ...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
Bayesian paradigm offers a conceptually simple and coherent system of statistical inference based on...
Markov chain Monte Carlo (MCMC) is the most widely used method of estimating joint posterior distrib...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...