The AstroStat Slog » EM algorithm http://hea-www.harvard.edu/AstroStat/slog Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders Fri, 09 Sep 2011 17:05:33 +0000 en-US hourly 1 http://wordpress.org/?v=3.4 [ArXiv] Sparse Poisson Intensity Reconstruction Algorithms http://hea-www.harvard.edu/AstroStat/slog/2009/arxiv-sparse-poisson-intensity-reconstruction-algorithms/ http://hea-www.harvard.edu/AstroStat/slog/2009/arxiv-sparse-poisson-intensity-reconstruction-algorithms/#comments Thu, 07 May 2009 16:14:39 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/?p=2498 One of [ArXiv] papers from yesterday whose title might drag lots of attentions from astronomers. Furthermore, it’s a short paper.
[arxiv:math.CO:0905.0483] by Harmany, Marcia, and Willet.

Estimating f under “Sparse Poisson Intensity” condition is an frequently appearing topic in high energy astrophysics data analysis. Some might like to check references in the paper, which offer solutions to compressed sensing problems with different kinds of sparsity, minimization approaches, and constraints on f.

Apart from the technical details, the first two sentences from the conclusion,

We have developed computational approaches for signal reconstruction from photon-limited measurements – a situation prevalent in many practical settings. Our method optimizes a regularized Poisson likelihood under nonnegativity constraints

tempt me to study and try their algorithm.

]]> http://hea-www.harvard.edu/AstroStat/slog/2009/arxiv-sparse-poisson-intensity-reconstruction-algorithms/feed/ 0 my first AAS. V. measurement error and EM http://hea-www.harvard.edu/AstroStat/slog/2008/first-aas-measurement-error-and-em/ http://hea-www.harvard.edu/AstroStat/slog/2008/first-aas-measurement-error-and-em/#comments Fri, 20 Jun 2008 03:46:05 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/?p=336 While discussing different view points on the term, clustering, one of the conversers led me to his colleague’s poster. This poster (I don’t remember its title and abstract) was my favorite from all posters in the meeting.

He rewrote the EM algorithm to include measurement errors in redshifts. Indexed parameters associated with different redshifts and corresponding standard deviations (measurement errors, treated as nuisance parameters) were included in the likelihood function that corrected bias and manifested bimodality in the LFs clearly at the different evolutionary stages.

I encouraged him to talk statisticians to characterize and to generalize his measurement error included likelihoods, and to optimize his EM algorithm. Because of approximations in algebra and the many parameters from measurement errors from redshifts, some assumptions and constraints were imposed intensively and I thought a collaboration with statisticians suits to get around constraints and to generalize his measurement error included likelihood.

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