Archive for September 2008


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 ‘Make3D’ »

[Q] Objectivity and Frequentist Statistics

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 ‘[Q] Objectivity and Frequentist Statistics’ »

There and back again

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 ‘There and back again’ »

Quintessential Contributions

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

Continue reading ‘Quintessential Contributions’ »

Classification and Clustering

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 ‘Classification and Clustering’ »

A History of Markov Chain Monte Carlo

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 ‘A History of Markov Chain Monte Carlo’ »


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 ‘BUGS’ »

[Book] pattern recognition and machine learning

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 ‘[Book] pattern recognition and machine learning’ »

appealing eyes == powerful method

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 ‘appealing eyes == powerful method’ »

Parametric Bootstrap vs. Nonparametric Bootstrap

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.

Continue reading ‘Parametric Bootstrap vs. Nonparametric Bootstrap’ »

Why Gaussianity?

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

Continue reading ‘Why Gaussianity?’ »

LHC First Beam

10:00am local time, Sept. 10th, 2008
As the first light from Fermi or GLAST, LHC First Beam is also a big moment for particle physicists. Find more from Continue reading ‘LHC First Beam’ »

A Confession from a former “keV” Junkie: 1. It’s a Plague.

(Inspired by vlk’s “keV vs keV”)

Beside the obvious benefit of confusing the public and colleagues in other fields, the apparent chaotic use of physical units like keV and Kevin has an addictive convenience beyond a simple matter of convention. Yes, I said “convenience”. Continue reading ‘A Confession from a former “keV” Junkie: 1. It’s a Plague.’ »

A Conversation with Peter Huber

The problem with data analysis is of course that it is a performing art. It is not something you easily write a paper on; rather, it is something you do. And so it is difficult to publish.

quoted from this conversation Continue reading ‘A Conversation with Peter Huber’ »

An anecdote on entrophy

My greatest concern was what to call it. I thought of calling it “information”, but the word was overly used, so I decided to call it “uncertainty”. When I discussed it with John von Neumann, he had a better idea. Von Neumann told me, “You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, nobody knows what entropy really is, so in a debate you will always have the advantage.”

Continue reading ‘An anecdote on entrophy’ »