Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders
Archive for the ‘Bayesian’ Category.
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
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’ »
I didn’t realize this post was sitting for a month during which I almost neglected the slog. As if great books about probability and information theory for statisticians and engineers exist, I believe there are great statistical physics books for physicists. On the other hand, relatively less exist that introduce one subject to the other kind audience. In this regard, I thought the lecture note can be useful.
[arxiv:physics.data-an:0808.0012]
Lectures on Probability, Entropy, and Statistical Physics by Ariel Caticha
Abstract:
As mentioned before, background subtraction plays a big role in astrophysical analyses. For a variety of reasons, it is not a good idea to subtract out background counts from source counts, especially in the low-counts Poisson regime. What Bayesians recommend instead is to set up a model for the intensity of the source and the background and to infer these intensities given the data. Continue reading ‘Background Subtraction, the Sequel [Eqn]’ »
Eight astro-ph papers and two statistics paper are listed this week. One statistics paper discusses detecting filaments and the other talks about maximum likelihood estimation of satellite images (clouds). Continue reading ‘[ArXiv] 4th week, May 2008’ »
Not many this week, but there’s a great read. Continue reading ‘[ArXiv] 3rd week, May 2008’ »
Markov chain Monte Carlo became the most frequent and stable statistical application in astronomy. It will be useful collecting tutorials from both professions. Continue reading ‘[ArXiv] 2nd week, Apr. 2008’ »
Really, there is no point in extracting a sentence here and there, go read the whole thing:
“Why I don’t like Bayesian Statistics”
- Andrew Gelman
Oh, alright, here’s one:
I can’t keep track of what all those Bayesians are doing nowadays–unfortunately, all sorts of people are being seduced by the promises of automatic inference through the “magic of MCMC”–but I wish they would all just stop already and get back to doing statistics the way it should be done, back in the old days when a p-value stood for something, when a confidence interval meant what it said, and statistical bias was something to eliminate, not something to embrace.
I began to study statistics with the notion that statistics is the study of information (retrieval) and a part of information is uncertainty which is taken for granted in our random world. Probably, it is the other way around; information is a part of uncertainty. Could this be the difference between Bayesian and frequentist?
The statistician’s task is to articulate the scientist’s uncertainties in the language of probability, and then to compute with the numbers found: cited from Continue reading ‘Statistics is the study of uncertainty’ »
Irrelevant to astrostatistics but interesting for baseball lovers.
[stat.AP:0802.4317] Jensen, Shirley, & Wyner
Bayesball: A Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball
With the 5th year WMAP data release, there were many WMAP related papers and among them, most statistical papers are listed. Continue reading ‘[ArXiv] 1st week, Mar. 2008’ »
This is a quite long paper that I separated from [Arvix] 4th week, Feb. 2008:
[astro-ph:0802.3916] P. Carvalho, G. Rocha, & M.P.Hobso
A fast Bayesian approach to discrete object detection in astronomical datasets - PowellSnakes I
As the title suggests, it describes Bayesian source detection and provides me a chance to learn the foundation of source detection in astronomy. Continue reading ‘[ArXiv] A fast Bayesian object detection’ »
Astronomers have developed their ways of processing signals almost independent to but sometimes collaboratively with engineers, although the fundamental of signal processing is same: extracting information. Doubtlessly, these two parallel roads of astronomers’ and engineers’ have been pointing opposite directions: one toward the sky and the other to the earth. Nevertheless, without an intensive argument, we could say that somewhat statistics has played the medium of signal processing for both scientists and engineers. This particular issue of IEEE signal processing magazine may shed lights for astronomers interested in signal processing and statistics outside the astronomical society.
IEEE Signal Processing Magazine Jul. 2007 Vol 24 Issue 4: Bootstrap methods in signal processing
This link will show the table of contents and provide links to articles; however, the access to papers requires IEEE Xplore subscription via libraries or individual IEEE memberships). Here, I’d like to attempt to introduce some articles and tutorials.
Continue reading ‘Signal Processing and Bootstrap’ »
Hyunsook drew attention to this paper (arXiv:0709.4531v1) by Brad Schaefer on the underdispersed measurements of the distances to LMC. He makes a compelling case that since 2002 published numbers in the literature have been hewing to an “acceptable number”, possibly in an unconscious effort to pass muster with their referees. Essentially, the distribution of the best-fit distances are much more closely clustered than you would expect from the quoted sizes of the error bars. Continue reading ‘“you are biased, I have an informative prior”’ »
Not knowing much about java and java applets in a software development and its web/internet publicizing, I cannot comment what is more efficient. Nevertheless, I thought that PHP would do the similar job in a simpler fashion and the followings may provide some ideas and solutions for publicizing statistical methods through websites based on Bayesian Inference.
Continue reading ‘Implement Bayesian inference using PHP’ »