Nested Sampling: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
| Line 1: | Line 1: | ||
Nested sampling is an alternative method of MCMC sampling used to make inferences about models from data. Specifically Nested sampling approaches inference with the goal of calculating the Bayesian Evidence for model comparison. However, the joint posterior for the model parameters are also generated in the process. | Nested sampling is an alternative method of MCMC sampling used to make inferences about models from data. Specifically Nested sampling approaches inference with the goal of calculating the Bayesian Evidence for model comparison. However, the joint posterior for the model parameters are also generated in the process. | ||
Slides from a graduate lecture given by James Allison can be found | Slides from a graduate lecture given by James Allison can be found [[here:Nested sampling lecture 2016.pdf ]] | ||
Revision as of 16:33, 6 November 2020
Nested sampling is an alternative method of MCMC sampling used to make inferences about models from data. Specifically Nested sampling approaches inference with the goal of calculating the Bayesian Evidence for model comparison. However, the joint posterior for the model parameters are also generated in the process.
Slides from a graduate lecture given by James Allison can be found here:Nested sampling lecture 2016.pdf