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Bayesian machine learning workshop, featuring an astronomy application

I’ve copied below the text of an ISBA announcement for the first workshop in a new series addressing Bayesian methods for machine learning. It builds on the model of the earlier well-known “Bayesian case studies” workshops, where just a few applications are featured at each workshop, with the format tailored to produce a lot of back-and-forth between application scientists and statisticians.

One of the three topics for the October workshop is titled “Calibrating the Universe: a Bayesian Uncertainty Analysis of a Galaxy Simulation.” This sounds a bit reminiscent of the “cosmic calibration” work by a collaboration of astronomers and statisticians at Los Alamos. They are using a combination of parametric and nonparametric Bayesian methods and dimensional reduction and experimental design techniques to infer cosmological parameters from CMB, large scale structure, and Type Ia supernova data. Despite the similarity in nomenclature, this appears to be a different team and a different application. However, from what I can glean from the team, it’s the same kind of problem: implementing a parametric Bayesian analysis with a computationally expensive model, by building a fast nonparametric “emulator” for the model. Should be interesting.

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July Workshop on Bayesian & Maximum Entropy Methods

The 29th International Workshop on Bayesian and Maximum Entropy Methods in Science and Engineering will be held 5-10 July at the University of Mississippi (“Ole Miss”), in the quaint university town of Oxford, MS. The organizing committee is currently accepting submissions of abstracts for both oral and poster presentations. Visit the MaxEnt 2009 web site for more detailed information.

I’m on the organizing committee and I’m excited about this year’s meeting. It is covering a broad range of areas with some exciting speakers. Topics include straightforward applications of parametric Bayesian methods, nonparametric methods, Bayesian computation (including the nested sampling algorithm currently making an impact in cosmology), experimental design, statistical mechanics, foundations of statistics, and even some talks by leaders in the areas of the foundations of statistical mechanics and the interpretation of quantum mechanics. I’m very much looking forward to this year’s meeting, and I urge any interested AstroStat Slog readers to submit an abstract (the deadline is imminent, but if it takes you a couple days longer to come up with something, do send it).

For those new to Bayesian methods, note that the workshop begins with a full day of tutorial lectures.

DOE Petascale Data Analysis Program

Woncheol Jang pointed me to the following web site describing a proposal opportunity at DOE that may be of interest to readers of this list:

Mathematics for Analysis of Petascale Data

The Office of Advanced Scientific Computing Research (ASCR) of the Office of Science (SC), U.S. Department of Energy (DOE), hereby announces its interest in receiving grant applications for research addressing the mathematical challenges involved in extracting insights from extremely large datasets (“petascale data”) and investigating fundamental issues in finding key features and understanding the relationships between those features.

All applications should address the potential for advances in mathematical methods or numerical algorithms and not just the application of methods and algorithms to a specific science problem, no matter how challenging.

This solicitation seeks applications for basic research in mathematical models, methods and tools for the representation, analysis, and understanding of petascale data.

They specifically mention data from physics simulations and observational data from cosmology as examples in the description.

Letters of Intent (required) are due 15 April, proposals are due 29 May. $4M is available for FY09; awards may be for up to 3 yr.

Meet at January AAS meeting to organize a white paper for Astro2010

Hello Sloggers,

Every decade, the National Research Council (under the auspices of the National Academies) convenes a panel to survey the state of astronomy and astrophysics, and to recommend plans and funding priorities for the subsequent decade. The resulting Decadal Survey document has a profound influence on funding of astronomy research at every level. The process for the 2010 decadal survey has begun; Roger Blandford will discuss it at the January 2009 AAS meeting (AAS decadal survey session, Tues, 6 Jan, 8:30am). The National Academies web site hosts a page for the Astro2010 Decadal Survey with more information.

White papers authored by individuals and groups in the astronomical community are a major source of input for the review panel. I would like to lead the effort on a collaborative white paper urging explicit, targeted support for (interdisciplinary) astrostatistics research (perhaps broadened to “astroinformatics” or “astronomical data analysis”). I would like to meet with any of you who would like to co-sign such a white paper, and help author it (as your resources allow). I think the AAS meeting offers a great opportunity for us to meet in person to start fleshing out ideas for the white paper, to be subsequently fleshed out via online interaction.

Here I’d like to discuss when to meet at AAS. Note that some Sloggers are participating in an astrostatistics special session, “Meaning from Surveys and Population Studies”, Monday, 2-3:30pm. In principle, since some of us will already be gathered there, it could make sense to meet afterward somewhere; but there are important prize lectures right afterward that I, for one, would like to hear. Other possibilities include lunch or dinner that day (Monday), or perhaps lunch or dinner the next day, after we’ve all heard Roger Blandford’s presentation on how the survey will work this year.

I have some concrete ideas for the white paper, and I’m sure some of you do, too. But here and now, let’s not get into content; let’s just organize a meeting at AAS.

With that, the floor is open for suggestions on a good meeting time/venue.