The AstroStat Slog

I bet there are various scams. One of them is automatic user registration. This blog requires a registration for contributing free of approval comments unless one does not put many web links. Recently, there were frequent anonymous user registrations. What I mean by anonymous is that I don’t see their names or part of identities (for example, someone uses initials of their names in their email accounts or uses email accounts from their affiliations). This slog is open to anyone who is interested in AstroStatistics, although not many are currently active. Upon your request, this can be changed very simply and you immediately start writing your ideas about AstroStatistics. However, I must restrict those scams from now on. Please, provide me a small information about you if you do not want to be eliminated after your registration. As I mentioned, the information does not require your full name, nor email account of academic institution. When you register, use your email account that you use daily bases, not the ones that look like results from phishing.

All of a sudden, partially owing to a thought provoking talk about visualization by Felice Frankel at IIC, I recollected a book, The Grammar of Graphics by Leland Wilkinson (2nd Ed. – I partially read the 1st ed. and felt little of use several years ago because there seemed no link for visualization of data from astronomy.) …Continue reading»

In order to understand a learning procedure statistically it is necessary to identify two important aspects: its structural model and its error model. The former is most important since it determines the function space of the approximator, thereby characterizing the class of functions or hypothesis that can be accurately approximated with it. The error model specifies the distribution of random departures of sampled data from the structural model.

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People of experience would say very differently and wisely against what I’m going to discuss now. This post only combines two small cross sections of each branch of two trees, astronomy and statistics. …Continue reading»

At least two images for reconstructing a 3D scene is a conventional belief. Yet, we do know that our eyes reconstruct 3D scenes from various single snap shot images, just with one picture. Based on our perception and learning ability or our internal pattern recognition ability, a few groups of people have been trying to reconstruct a 3D image from one still image picture. Luckily you can test such progress, reconstructing a 3D scene from a single still image at Make3D (a click brings you to Make3D at Stanford). …Continue reading»

Is there an objective method to combine measurements of the same quantity obtained with different instruments?

Suppose you have a set of N₁ measurements obtained with one detector, and another set of N₂ measurements obtained with a second detector. And let’s say you wanted something as simple as an estimate of the mean of the quantity (say the intensity) being measured. Let us further stipulate that the measurement errors of each of the points is similar in magnitude and neither instrument displays any odd behavior. How does one combine the two datasets without appealing to subjective biases about the reliability or otherwise of the two instruments? …Continue reading»

The absolutely phenomenal webcomic XKCD hits a home run again, this time sketching out the spatial structure of the Universe all the way from here to The Edge .. in log scale. …Continue reading»

To my personal thoughts, the history of astronomy is more interesting than the history of statistics. This may change tomorrow. Harvard statistics department (chair Xiao-Li Meng) organizes a symposium titled

Quintessential Contributions:
Celebrating Major Birthdays of Statistical Ideas and Their Inventors
When: Saturday, September 27, 2008, 9:45 AM – 5:00 PM
Where: Radcliffe Gymnasium, 18 Mason Street, Cambridge, MA

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Another deduced conclusion from reading preprints listed in arxiv/astro-ph is that astronomers tend to confuse classification and clustering and to mix up methodologies. They tend to think any algorithms from classification or clustering analysis serve their purpose since both analysis algorithms, no matter what, look like a black box. I mean a black box as in neural network, which is one of classification algorithms. …Continue reading»

I’ve been joking about the astronomers’ fashion in writing Markov chain Monte Carlo (MCMC). Frequently, MCMC was represented by Monte Carlo Markov Chain in astronomical journals. I was curious about the history of this new creation. Overall, I thought it would be worth to learn more about the history of MCMC and this paper was up in arxiv: …Continue reading»

Astronomers tend to think in Bayesian way, but their Bayesian implementation is very limited. OpenBUGS, WinBUGS, GeoBUGS (BUGS for geostatistics; for example, modeling spatial distribution), R2WinBUGS (R BUGS wrapper) or PyBUGS (Python BUGS wrapper) could boost their Bayesian eagerness. Oh, by the way, BUGS stands for Bayesian inference Using Gibbs Sampling. …Continue reading»

A nice book by Christopher Bishop.
While I was reading abstracts and papers from astro-ph, I saw many applications of algorithms from pattern recognition and machine learning (PRML). The frequency will increase as large scale survey projects numerate, where recommending a good textbook or a reference in the field seems timely. …Continue reading»

To claim results are powerful statistically, astronomers highly rely on eyeballing techniques (need apprenticeship to acquire skills but look subjective to me without such training). Some cases, I know actual statistical tests to support or to dissuade those claims. Hence, I believe astronomers are well aware of those statistical tests. I guess they are afraid that those statistics may reject their claims or are not powerful enough in numeric metrics. Instead, they spend efforts to make graphics more appealing. …Continue reading»

The following footnotes are from one of Prof. Babu’s slides but I do not recall which occasion he presented the content.

– In the XSPEC packages, the parametric bootstrap is command FAKEIT, which makes Monte Carlo simulation of specified spectral model.
– XSPEC does not provide a nonparametric bootstrap capability.

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Physicists believe that the Gaussian law has been proved in mathematics while mathematicians think that it was experimentally established in physics — Henri Poincare

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