Nested Sampling: Difference between revisions
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Nested sampling is an alternative method of MCMC sampling used to make inferences about models from data | Nested sampling is an alternative method of MCMC sampling used to make inferences about models from data. | ||
Slides from a graduate lecture given by James Allison can be found | 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 [https://johannesbuchner.github.io/PyMultiNest/]. Note that MultiNest is notoriously challenging to compile and install, and so you may just wish to use the installed version on Glamdring. | |||
Slides from a graduate lecture given by James Allison can be found at [[File:Nested sampling lecture 2016.pdf]] | |||
Useful references: | |||
John Skilling's website [https://www.inference.org.uk/bayesys/] | |||
Feroz & Hobson (2008) [https://ui.adsabs.harvard.edu/abs/2008MNRAS.384..449F/] | |||
Feroz et al. (2009) [https://ui.adsabs.harvard.edu/abs/2009MNRAS.398.1601F/] | |||
Feroz et al. (2019) [https://ui.adsabs.harvard.edu/abs/2019OJAp....2E..10F] | |||
Latest revision as of 17:19, 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 you may just wish to use the installed version on Glamdring.
Slides from a graduate lecture given by James Allison can be found at 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]