We congratulate the authors on a magnificent paper, providing a nicely paced introduction to Markov chain Monte Carlo and its applications, together with several new ideas. In particular the class of pairwise difference priors is bound to have a substantial impact on future applied work. Other ideas given less prominence in the paper are also valuable, for example, the construction of simultaneous credible regions based on MCMC output. There are several issues which we wish to comment on in detail
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
While the previous chapter (Robert and Rousseau, 2010) addressed the foundational aspects of Bayesia...
We thoroughly enjoyed reading this paper and are delighted to contribute to its discussion. The auth...
These notes provide an introduction to Markov chain Monte Carlo methods that are useful in both Baye...
This paper reviews the way statisticians use Markov Chain Monte Carlo (MCMC) methods. These techniq...
7 pages, 4 figuresThis is the compilation of our comments submitted to the Journal of the Royal Stat...
7 pages, 4 figuresThis is the compilation of our comments submitted to the Journal of the Royal Stat...
This paper is also the originator of the Markov Chain Monte Carlo methods developed in the following...
We merge in this note our two discussions about the Read Paper "Particle Markov chain Monte Carlo" (...
We merge in this note our two discussions about the Read Paper “Particle Markov chain Monte Carlo ” ...
Since Bayes ’ Theorem was first published in 1762, many have argued for the Bayesian paradigm on pur...
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
While the previous chapter (Robert and Rousseau, 2010) addressed the foundational aspects of Bayesia...
We thoroughly enjoyed reading this paper and are delighted to contribute to its discussion. The auth...
These notes provide an introduction to Markov chain Monte Carlo methods that are useful in both Baye...
This paper reviews the way statisticians use Markov Chain Monte Carlo (MCMC) methods. These techniq...
7 pages, 4 figuresThis is the compilation of our comments submitted to the Journal of the Royal Stat...
7 pages, 4 figuresThis is the compilation of our comments submitted to the Journal of the Royal Stat...
This paper is also the originator of the Markov Chain Monte Carlo methods developed in the following...
We merge in this note our two discussions about the Read Paper "Particle Markov chain Monte Carlo" (...
We merge in this note our two discussions about the Read Paper “Particle Markov chain Monte Carlo ” ...
Since Bayes ’ Theorem was first published in 1762, many have argued for the Bayesian paradigm on pur...
The application of Bayes' Theorem to signal processing provides a consistent framework for proceedin...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
This article considers Markov chain computational methods for incorporating uncertainty about the d...