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nested sampling via SMC
“We show that by implementing a special type of [sequential Monte Carlo] sampler that takes two im-portance sampling paths at each iteration, one obtains an analogous SMC method to [nested sampling]...
View ArticleNested Sampling SMC [a reply]
Here is a response from Robert Salomone following my comments of the earlier day (and pointing out I already commented the paper two years ago): You may be interested to know that we are at the tail...
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scalable Langevin exact algorithm [armchair Read Paper]
So, Murray Pollock, Paul Fearnhead, Adam M. Johansen and Gareth O. Roberts presented their Read Paper with discussions on the Wednesday aft! With a well-sized if virtual audience of nearly a hundred...
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the surprisingly overlooked efficiency of SMC
At the Laplace demon’s seminar today (whose cool name I cannot tire of!), Nicolas Chopin gave a webinar with the above equally cool title. And the first slide debunking myths about SMC’s: The second...
View Articleaveraged acceptance ratios
In another recent arXival, Christophe Andrieu, Sinan Yıldırım, Arnaud Doucet, and Nicolas Chopin study the impact of averaging estimators of acceptance ratios in Metropolis-Hastings algorithms. (It is...
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population quasi-Monte Carlo
“Population Monte Carlo (PMC) is an important class of Monte Carlo methods, which utilizes a population of proposals to generate weighted samples that approximate the target distribution” A return of...
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sandwiching a marginal
When working recently on a paper for estimating the marginal likelihood, I was pointed out this earlier 2015 paper by Roger Grosse, Zoubin Ghahramani and Ryan Adams, which had escaped till now. The...
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Bayes factors revisited
“Bayes factor analyses are highly sensitive to and crucially depend on prior assumptions about model parameters (…) Note that the dependency of Bayes factors on the prior goes beyond the dependency...
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EM degeneracy
At the MHC 2021 conference today (to which I biked to attend for real!, first time since BayesComp!) I listened to Christophe Biernacki exposing the dangers of EM applied to mixtures in the presence of...
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ordered allocation sampler
Recently, Pierpaolo De Blasi and María Gil-Leyva arXived a proposal for a novel Gibbs sampler for mixture models. In both finite and infinite mixture models. In connection with Pitman (1996) theory of...
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living on the edge [of the canal]
Last month, Roberto Casarin, Radu Craiu, Lorenzo Frattarolo and myself posted an arXiv paper on a unified approach to antithetic sampling. To which I mostly and modestly contributed while visiting...
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evidence estimation in finite and infinite mixture models
Adrien Hairault (PhD student at Dauphine), Judith and I just arXived a new paper on evidence estimation for mixtures. This may sound like a well-trodden path that I have repeatedly explored in the...
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Fusion at CIRM
Today is the first day of the FUSION workshop Rémi Bardenet and myself organised. Due to schedule clashes, I will alas not be there, since [no alas!] at the BNP conference in Chili. The program and...
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séminaire parisien de statistique [09/01/23]
I had missed the séminaire parisien de statistique for most of the Fall semester, hence was determined to attend the first session of the year 2023, the more because the talks were close to my...
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BayesComp²³ [aka MCMski⁶]
The main BayesComp meeting started right after the ABC workshop and went on at a grueling pace, and offered a constant conundrum as to which of the four sessions to attend, the more when trying to...
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ellis unconference [not in Hawai’i]
As ICML 2023 is happening this week, in Hawai’i, many did not have the opportunity to get there, for whatever reason, and hence the ellis (European Lab for Learning {and} Intelligent Systems] board...
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sequential meetings in Edinburgh
There will be not one but two consecutive events in Edinburgh next May²⁴ on sequential Monte Carlo methods! Both hosted by the fantastic International Centre for Mathematical Sciences (ICMS) in...
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futuristic statistical science [editorial]
This special issue of Statistical Science is devoted to the future of Bayesian computational statistics, from several perspectives. It involves a large group of researchers who contributed to...
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simulation as optimization [by kernel gradient descent]
Yesterday, which proved an unseasonal bright, warm, day, I biked (with a new wheel!) to the east of Paris—in the Gare de Lyon district where I lived for three years in the 1980’s—to attend a Mokaplan...
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