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latent nested nonparametric priors

September 23, 2019

A paper on an extended type of non-parametric priors by Camerlenghi et al. [all good friends!] is about to appear in Bayesian Analysis, with a discussion open for contributions (until October 15). While a fairly theoretical piece of work, it validates a Bayesian approach for non-parametric clustering of separate populations with, broadly speaking, common clusters. […]

likelihood free nested sampling

April 26, 2019

A recent paper by Mikelson and Khammash found on bioRxiv considers the (paradoxical?) mixture of nested sampling and intractable likelihood. They however cover only the case when a particle filter or another unbiased estimator of the likelihood function can be found. Unless I am missing something in the paper, this seems a very costly and […]

dynamic nested sampling for stars

April 12, 2019

In the sequel of earlier nested sampling packages, like MultiNest, Joshua Speagle has written a new package called dynesty that manages dynamic nested sampling, primarily intended for astronomical applications. Which is the field where nested sampling is the most popular. One of the first remarks in the paper is that nested sampling can be more […]

unbiased consistent nested sampling via sequential Monte Carlo [a reply]

June 13, 2018

Rob Salomone sent me the following reply on my comments of yesterday about their recently arXived paper. Our main goal in the paper was to show that Nested Sampling (when interpreted a certain way) is really just a member of a larger class of SMC algorithms, and exploring the consequences of that. We should point out […]

unbiased consistent nested sampling via sequential Monte Carlo

June 12, 2018

“Moreover, estimates of the marginal likelihood are unbiased.” (p.2) Rob Salomone, Leah South, Chris Drovandi and Dirk Kroese (from QUT and UQ, Brisbane) recently arXived a paper that frames the nested sampling in such a way that marginal likelihoods can be unbiasedly (and consistently) estimated. “Why isn’t nested sampling more popular with statisticians?” (p.7) A […]