Add to ORKGThe emergence in the past years of Bayesian analysis in many methodological and applied fields as the solution to the modeling of complex problems cannot be dissociated from major changes in its computational implementation. We show in this review how the advances in both Bayesian analysis and statistical computation are intermingled
Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis....
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
Recent decades have seen enormous improvements in computational inference for statistical models; th...
If, in the mid 1980's, one had asked the average statistician about the di-culties of using Bayesian...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
This is a revised version of a chapter written for the Handbook of Computational Statistics, edited ...
The past decades have seen enormous im-provements in computational inference based on sta-tistical m...
Current statistical methods for facilitating data-driven decision making are too computationally int...
In this chapter, we will first present the most standard computational challenges met in Bayesian St...
While the previous chapter (Robert and Rousseau, 2010) addressed the foundational aspects of Bayesia...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis....
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
Recent decades have seen enormous improvements in computational inference for statistical models; th...
If, in the mid 1980's, one had asked the average statistician about the di-culties of using Bayesian...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
This is a revised version of a chapter written for the Handbook of Computational Statistics, edited ...
The past decades have seen enormous im-provements in computational inference based on sta-tistical m...
Current statistical methods for facilitating data-driven decision making are too computationally int...
In this chapter, we will first present the most standard computational challenges met in Bayesian St...
While the previous chapter (Robert and Rousseau, 2010) addressed the foundational aspects of Bayesia...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis....
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...