Posts Tagged ‘ University life ’

likelihood-free inference in high-dimensional models

August 31, 2015
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likelihood-free inference in high-dimensional models

“…for a general linear model (GLM), a single linear function is a sufficient statistic for each associated parameter…” The recently arXived paper “Likelihood-free inference in high-dimensional models“, by Kousathanas et al. (July 2015), proposes an ABC resolution of the dimensionality curse [when the dimension of the parameter and of the corresponding summary statistics] by turning […]

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abcfr 0.9-3

August 26, 2015
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abcfr 0.9-3

In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the […]

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STAN trailer [PG+53]

August 13, 2015
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STAN trailer [PG+53]

[Heading off to mountainous areas with no Internet or phone connection, I posted a series of entries for the following week, starting with this brilliant trailer of Michael:] Filed under: Kids, R, Statistics, University life Tagged: Andrew Gelman, Hami...

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JSM 2015 [day #2]

August 11, 2015
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JSM 2015 [day #2]

Today, at JSM 2015, in Seattle, I attended several Bayesian sessions, having sadly missed the Dennis Lindley memorial session yesterday, as it clashed with my own session. In the morning sessions on Bayesian model choice, David Rossell (Warwick) defended non-local priors à la Johnson (& Rossell) as having better frequentist properties. Although I appreciate the concept […]

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JSM 2015 [day #1]

August 10, 2015
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JSM 2015 [day #1]

This afternoon, at JSM 2015, in Seattle, we had the Bayesian Computation I and II sessions that Omiros Papaspiliopoulos and myself put together (sponsored by IMS and ISBA). Despite this being Sunday and hence having some of the participants still arriving, the sessions went on well in terms of audience. Thanks to Mark Girolami’s strict […]

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delayed in Seattle

August 8, 2015
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delayed in Seattle

Here are the slides of my talk on delayed acceptance I present this afternoon at JSM 2015, in Seattle, in the Bayesian Computation I (2pm, room CC-4C1) and II (4pm, room CC-3A) sessions Omiros Papaspiliopoulos and myself put together (sponsored by IMS and ISBA):Filed under: Books, R, Statistics, Travel, University life Tagged: American Statistical Association, […]

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Moment conditions and Bayesian nonparametrics

August 5, 2015
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Moment conditions and Bayesian nonparametrics

Luke Bornn, Neil Shephard, and Reza Solgi (all from Harvard) have arXived a pretty interesting paper on simulating targets on a zero measure set. Although it is not initially presented this way, but rather in non-parametric terms as moment conditions where θ is the parameter of the sampling distribution, constrained by the value of β. (Which […]

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Le Monde puzzle [#920]

July 22, 2015
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Le Monde puzzle [#920]

A puzzling Le Monde mathematical puzzle (or blame the heat wave): A pocket calculator with ten keys (0,1,…,9) starts with a random digit n between 0 and 9. A number on the screen can then be modified into another number by two rules: 1. pressing k changes the k-th digit v whenever it exists into […]

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MCMskv, Lenzerheide, 4-7 Jan., 2016 [news #1]

July 19, 2015
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MCMskv, Lenzerheide, 4-7 Jan., 2016 [news #1]

The BayesComp MCMski V [or MCMskv for short] has now its official website, once again maintained by Merrill Lietchy from Drexel University, Philadelphia, and registration is even open! The call for contributed sessions is now over, while the call for posters remains open until the very end. The novelty from the previous post is that there […]

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Leave the Pima Indians alone!

July 14, 2015
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Leave the Pima Indians alone!

“…our findings shall lead to us be critical of certain current practices. Specifically, most papers seem content with comparing some new algorithm with Gibbs sampling, on a few small datasets, such as the well-known Pima Indians diabetes dataset (8 covariates). But we shall see that, for such datasets, approaches that are even more basic than […]

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