Archive for OxWaSP
not-so-new bike!
Posted in pictures, Travel with tags bargain, Go Sport, informal economy, Le Bon Coin, Oxford, OxWaSP, Paris, PariSanté campus, road bike, University of Oxford, used bike on August 23, 2025 by xi'an
why don’t you wear a suit?!
Posted in Books, Kids, pictures, Travel, University life with tags baby Trump, black Friday, CDT, Centre for Doctoral Training, department of statistics, Des Moines, French Navy, Iowa, John Cairns, journalism, MAGA, objectivity, Oxford, OxWaSP, PhD students, Racoon River Brewing Co., Solidarity with Ukraine, Trump administration, University of Oxford, US politics, USA, Volodymyr Zelenskiy, White House on March 4, 2025 by xi'an
distributed evidence
Posted in Books, pictures, Statistics, University life with tags Bayesian Analysis, Bayesian model choice, belief aggregation, CDT, conditionally conjugate models, conjugate priors, consensus Monte Carlo, CREST, Data augmentation, data privacy, evidence, importance sampling, infinite variance estimators, marginal likelihood, MCMC, OxWaSP, parallel processing, reversible jump MCMC, University of Cambridge on December 16, 2021 by xi'an
Alexander Buchholz (who did his PhD at CREST with Nicolas Chopin), Daniel Ahfock, and my friend Sylvia Richardson published a great paper on the distributed computation of Bayesian evidence in Bayesian Analysis. The setting is one of distributed data from several sources with no communication between them, which relates to consensus Monte Carlo even though model choice has not been particularly studied from that perspective. The authors operate under the assumption of conditionally conjugate models, i.e., the existence of a data augmentation scheme into an exponential family so that conjugate priors can be used. For a division of the data into S blocks, the fundamental identity in the paper is
where α is the normalising constant of the sub-prior exp{log[p(θ)]/S} and the other terms are associated with this prior. Under the conditionally conjugate assumption, the integral can be approximated based on the latent variables. Most interestingly, the associated variance is directly connected with the variance of
under the joint:
“The variance of the ratio measures the quality of the product of the conditional sub-posterior as an importance sample proposal distribution.”
Assuming this variance is finite (which is likely). An approximate alternative is proposed, namely to replace the exact sub-posterior with a Normal distribution, as in consensus Monte Carlo, which should obviously require some consideration as to which parameterisation of the model produces the “most normal” (or the least abnormal!) posterior. And ensures a finite variance in the importance sampling approximation (as ensured by the strong bounds in Proposition 5). A problem shared by the bridgesampling package.
“…if the error that comes from MCMC sampling is relatively small and that the shard sizes are large enough so that the quality of the subposterior normal approximation is reasonable, our suggested approach will result in good approximations of the full data set marginal likelihood.”
The resulting approximation can also be handy in conjunction with reversible jump MCMC, in the sense that RJMCMC algorithms can be run in parallel on different chunks or shards of the entire dataset. Although the computing gain may be reduced by the need for separate approximations.
Savage Award session today at JSM
Posted in Kids, Statistics, Travel, University life with tags approximate Bayesian inference, conflict of interest, ISBA, JSM 2020, machine learning, OxWaSP, probabilistic numerics, Savage award, University of Warwick, virtual conference, Warwick Statistics on August 3, 2020 by xi'an
Pleased to broadcast the JSM session dedicated to the 2020 Savage Award, taking place today at 13:00 ET (17:00 GMT), with two of the Savage nominees being former OxWaSP students (and Warwick PhD students). For those who have not registered for JSM, the talks are also available on Bayeslab. (As it happens, I was also a member of the committee this year, but do not think this could be deemed a CoI!)
| 112 | Mon, 8/3/2020, 1:00 PM – 2:50 PM | Virtual | |
| Savage Award Session — Invited Papers | |||
| International Society for Bayesian Analysis (ISBA) | |||
| Organizer(s): Maria De Iorio, University College London | |||
| Chair(s): Maria De Iorio, University College London | |||
| 1:05 PM | Bayesian Dynamic Modeling and Forecasting of Count Time Series Lindsay Berry, Berry Consultants |
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| 1:30 PM | Machine Learning Using Approximate Inference: Variational and Sequential Monte Carlo Methods Christian Andersson Naesseth, Columbia University |
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| 1:55 PM | Recent Advances in Bayesian Probabilistic Numerical Integration Francois-Xavier Briol, University College London |
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| 2:20 PM | Factor regression for dimensionality reduction and data integration techniques with applications to cancer data Alejandra Avalos Pacheco, Harvard Medical School |
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| 2:45 PM | Floor Discussion | ||
JB³ [Junior Bayes beyond the borders]
Posted in Books, Statistics, University life with tags Bayesian Analysis, Bayesian computation, BayesLab, Charles Stein, COVID-19, Italy, jBayes, JB³, jISBA, junior researchers, Milano, online seminar, OxWaSP, pandemic, Statistics without Borders, Stein's method, Università Bocconi, University College London, webinar on June 22, 2020 by xi'an
Bocconi and j-ISBA are launcing a webinar series for and by junior Bayesian researchers. The first talk is on 25 June, 25 at 3pm UTC/GMT (5pm CET) with Francois-Xavier Briol, one of the laureates of the 2020 Savage Thesis Prize (and a former graduate of OxWaSP, the Oxford-Warwick doctoral training program), on Stein’s method for Bayesian computation, with as a discussant Nicolas Chopin.
As pointed out on their webpage,
Due to the importance of the above endeavor, JB³ will continue after the health emergency as an annual series. It will include various refinements aimed at increasing the involvement of the whole junior Bayesian community and facilitating a broader participation to the online seminars all over the world via various online solutions.
Thanks to all my friends at Bocconi for running this experiment!
