Posts tagged ‘inference’

[MADS] Kalman Filter

I decide to discuss Kalman Filter a while ago for the slog after finding out that this popular methodology is rather underrepresented in astronomy. However, it is not completely missing from ADS. I see that the fulltext search and all bibliographic source search shows more results. Their use of Kalman filter, though, looked similar to the usage of “genetic algorithms” or “Bayes theorem.” Probably, the broad notion of Kalman filter makes it difficult my finding Kalman Filter applications by its name in astronomy since often wheels are reinvented (algorithms under different names have the same objective). Continue reading ‘[MADS] Kalman Filter’ »

a century ago

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’ »

[Announce] Heidelberg Summer School

From Christian Fendt comes this announcement:

First Announcement and Call for Applications

The “International Max Planck Research School for Astronomy & Cosmic Physics at the University of Heidelberg” (IMPRS-HD)

announces the

— 4th Heidelberg Summer School:

— Statistical Inferences from Astrophysical Data

— August 10-14, 2009

Continue reading ‘[Announce] Heidelberg Summer School’ »

accessing data, easier than before but…

Someone emailed me for globular cluster data sets I used in a proceeding paper, which was about how to determine the multi-modality (multiple populations) based on well known and new information criteria without binning the luminosity functions. I spent quite time to understand the data sets with suspicious numbers of globular cluster populations. On the other hand, obtaining globular cluster data sets was easy because of available data archives such as VizieR. Most data sets in charts/tables, I acquire those data from VizieR. In order to understand science behind those data sets, I check ADS. Well, actually it happens the other way around: check scientific background first to assess whether there is room for statistics, then search for available data sets. Continue reading ‘accessing data, easier than before but…’ »

Kepler and the Art of Astrophysical Inference

I recently discovered iTunesU, and I have to confess, I find it utterly fascinating. By golly, it is everything that they promised us that the internet would be. Informative, entertaining, and educational. What are the odds?!? Anyway, while poking around the myriad lectures, courses, and talks that are now online, I came across a popular Physics lecture series at UMichigan which listed a talk by one of my favorite speakers, Owen Gingerich. He had spoken about The Four Myths of the Copernican Revolution last November. It was, how shall we say, riveting.

Owen talks in detail about how the Copernican model came to supplant the Ptolemaic model. In particular, he describes how Kepler went from Ptolemaic epicycles to elliptical orbits. Contrary to general impression, Kepler did not fit ellipses to Tycho Brahe’s observations of Mars. The ellipticity is far too small for it to be fittable! But rather, he used logical reasoning to first offset Earth’s epicyle away from the center in order to avoid the so-called Martian Catastrophe, and then used the phenomenological constraint of the law of equal areas to infer that the path must be an ellipse.

This process, along with Galileo’s advocacy for the heliocentric system, demonstrates a telling fact about how Astrophysics is done in practice. Hyunsook once lamented that astronomers seem to be rather trigger happy with correlations and regressions, and everyone knows they don’t constitute proof of anything, so why do they do it? Owen says about 39 1/2 minutes into the lecture:

Here we have the fourth of the myths, that Galileo’s telescopic observations finally proved the motion of the earth and thereby, at last, established the truth of the Copernican system.

What I want to assure you is that, in general, science does not operate by proofs. You hear that an awful lot, about science looking for propositions that can be falsified, that proof plays this big role.. uh-uh. It is coherence of explanation, understanding things that are well-knit together; the broader the framework of knitting the things together, the more we are able to believe it.

Exactly! We build models, often with little justification in terms of experimental proof, and muddle along trying to make it fit into a coherent narrative. This is why statistics is looked upon with suspicion among astronomers, and why for centuries our mantra has been “if it takes statistics to prove it, it isn’t real!”

Prof. Brad Efron visits Harvard

Bradley Efron, Stanford University
11:00 AM, Friday, April 4, 2008
Sever Hall Rm. 103
Its abstract and other informations at
Continue reading ‘Prof. Brad Efron visits Harvard’ »

The GREAT08 Challenge

Grand statistical challenges seem to be all the rage nowadays. Following on the heels of the Banff Challenge (which dealt with figuring out how to set the bounds for the signal intensity that would result from the Higgs boson) comes the GREAT08 Challenge (arxiv/0802.1214) to deal with one of the major issues in observational Cosmology, the effect of dark matter. As Douglas Applegate puts it: Continue reading ‘The GREAT08 Challenge’ »

[ArXiv] Post Model Selection, Nov. 7, 2007

Today’s arxiv-stat email included papers by Poetscher and Leeb, who have been working on post model selection inference. Sometimes model selection is misled as a part of statistical inference. Simply, model selection can be considered as a step prior to inference. How you know your data are from chi-square distribution, or gamma distribution? (this is a model selection problem with nested models.) Should I estimate the degree of freedom, k from Chi-sq or α and β from gamma to know mean and error? Will the errors of the mean be same from both distributions? Continue reading ‘[ArXiv] Post Model Selection, Nov. 7, 2007’ »