Blog Archives

recycling accept-reject rejections (#2)

July 1, 2014
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recycling accept-reject rejections (#2)

Following yesterday’s post on Rao’s, Liu’s, and Dunson’s paper on a new approach to intractable normalising constants, and taking advantage of being in Warwick, I tested the method on a toy model, namely the posterior associated with n Student’s t observations with unknown location parameter μ and a flat prior, which is “naturally” bounded by

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R/Rmetrics in Paris [alas!]

June 29, 2014
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R/Rmetrics in Paris [alas!]

Today I gave a talk on Bayesian model choice in a fabulous 13th Century former monastery in the Latin Quarter of Paris… It is the Collège des Bernardins, close to Jussieu and Collège de France, unbelievably hidden to the point I was not aware of its existence despite having studied and worked in Jussieu since

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ABC model choice by random forests

June 24, 2014
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ABC model choice by random forests

After more than a year of collaboration, meetings, simulations, delays, switches,  visits, more delays, more simulations, discussions, and a final marathon wrapping day last Friday, Jean-Michel Marin, Pierre Pudlo,  and I at last completed our latest collaboration on ABC, with the central arguments that (a) using random forests is a good tool for choosing the

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revenge of the pigeons

June 23, 2014
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revenge of the pigeons

While I had not had kamikaze pigeons hitting my windows for quite a while…, it may be that one of them decided to move to biological warfare: when I came back from Edinburgh, my office at the University was in a terrible state as a bird had entered through a tiny window opening and wrecked

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trying to speed up Metropolis… and failing!

June 12, 2014
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trying to speed up Metropolis… and failing!

A while ago (but still after Iceland since I used the thorn rune as a math symbol!), I wrote the following post draft as a memo. Now that Marco Banterle, Clara Grazian and myself have completed our delayed acceptance paper, it may be of interest to some readers to see how a first attempt proved

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Statistical modeling and computation [apologies]

June 11, 2014
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Statistical modeling and computation [apologies]

In my book review of the recent book by Dirk Kroese and Joshua Chan,  Statistical Modeling and Computation, I mistakenly and persistently typed the name of the second author as Joshua Chen. This typo alas made it to the printed and on-line versions of the subsequent CHANCE 27(2) column. I am thus very much sorry

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checking for finite variance of importance samplers

June 10, 2014
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checking for finite variance of importance samplers

Over a welcomed curry yesterday night in Edinburgh I read this 2008 paper by Koopman, Shephard and Creal, testing the assumptions behind importance sampling, which purpose is to check on-line for (in)finite variance in an importance sampler, based on the empirical distribution of the importance weights. To this goal, the authors use the upper tail 

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Tools for Online Teaching

May 30, 2014
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Last semester (Fall 2014), I organized and taught an interdisciplinary, collaborative class titled Probability for Scientists. Getting 4 separate teachers on the same page was a challenge, but as scientists we're used to communicating over email, and ...

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estimation of deformations of densities

May 21, 2014
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estimation of deformations of densities

Today, Jean-Michel Loubes from Toulouse gave a seminar in Dauphine on the estimation of deformations using Wassertsein distances. This is functional data analysis, where samples from random transforms of the original density are observed towards estimating the baseline (or true) measure As a neophyte, I found the problem of interest if difficult to evaluate, in

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understanding complex and large industrial data (UCLID 2014)

May 15, 2014
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understanding complex and large industrial data (UCLID 2014)

Just received this announcement of the UCLID 2014 conference in Lancaster, July 1-2 2014: Understanding Complex and Large Industrial Data 2014, or UCLID, is a workshop which aims to provide an opportunity for academic researchers and industrial practitioners to work together and share ideas on the fast developing field of ‘big data’ analysis. This is

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