Nested Sampling: Difference between revisions
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The PyMultiNest wrapper can be found at [https://johannesbuchner.github.io/PyMultiNest/]. Note that MultiNest is notoriously challenging to compile and install, and so depending on how much time you have you may wish to just use the installed version on Glamdring. | The PyMultiNest wrapper can be found at [https://johannesbuchner.github.io/PyMultiNest/]. Note that MultiNest is notoriously challenging to compile and install, and so depending on how much time you have you may wish to just use the installed version on Glamdring. | ||
Slides from a graduate lecture given by James Allison can be found here | Slides from a graduate lecture given by James Allison can be found [[here|File:Nested sampling lecture 2016.pdf]] | ||
Useful references: | Useful references: | ||
Revision as of 18:09, 6 November 2020
Nested sampling is an alternative method of MCMC sampling used to make inferences about models from data.
Importantly, 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.
A commonly used implementation of nested sampling is MultiNest (multimodal nested sampling), which in addition to the vanilla algorithm allows multiple likelihood modes to be identified. That may be useful for certain applications such as source and spectral line finding.
The PyMultiNest wrapper can be found at [1]. Note that MultiNest is notoriously challenging to compile and install, and so depending on how much time you have you may wish to just use the installed version on Glamdring.
Slides from a graduate lecture given by James Allison can be found File:Nested sampling lecture 2016.pdf
Useful references:
John Skilling's website [2]
Feroz & Hobson (2008) [3]
Feroz et al. (2009) [4]
Feroz et al. (2019) [5]