Nested Sampling: Difference between revisions
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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. | 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, which in addition to the vanilla algorithm | 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 phttps://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 phttps://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. | ||
Revision as of 17:08, 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 phttps://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 File:Nested sampling lecture 2016.pdf
Useful references:
John Skilling's website [1]
Feroz & Hobson (2008) [2]
Feroz et al. (2009) [3]
Feroz et al. (2019) [4]