The AstroStat Slog

This simple law, despite my trial of full text search, was not showing in ADS. As discussed in systematic errors, astronomers, like physicists, show their error components in two additive terms; statistical error + systematic error. To explain such decomposition and to make error analysis statistically rigorous, the law of total variance (LTV) seems indispensable. Continue reading ‘[MADS] Law of Total Variance’ »

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.

Continue reading ‘Bayesian machine learning workshop, featuring an astronomy application’ »

Among billion objects in our Galaxy, outside the Earth, our Sun drags most attention from astronomers. These astronomers go by solar physicists, who enjoy the most abundant data including 400 year long sunspot counts. Their joy is not only originated from the fascinating, active, and unpredictable characteristics of the Sun but also attributed to its influence on our daily lives. Related to the latter, sometimes studying the conditions on the Sun is called space weather forecast. Continue reading ‘space weather’ »

My understandings of “robustness” from the education in statistics and from communicating with astronomers are hard to find a mutual interest. Can anyone help me to build a robust bridge to get over this abyss? Continue reading ‘Robust Statistics’ »

Almost 100 years ago, A.S. Eddington stated in his book Stellar Movements (1914) that

…in calculating the mean error of a series of observations it is preferable to use the simple mean residual irrespective of sign rather than the mean square residual

Such eminent astronomer said already least absolute deviation over chi-square, if I match simple mean residual and mean square residual to relevant methodologies, in order. Continue reading ‘a century ago’ »

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.
Continue reading ‘[ArXiv] Sparse Poisson Intensity Reconstruction Algorithms’ »

For someone who doesn’t know any grammar, I can be a bit of a Grammar nazi sometimes. And one of my pet peeves is when people use the word data in the singular. No! Data are!

Or so I used to believe. Continue reading ‘Datums’ »