Archive for the ‘Bayesian’ Category.

coin toss with a twist

Here’s a cool illustration of how to use Bayesian analysis in the limit of very little data, when inferences are necessarily dominated by the prior. The question, via Tom Moertel, is: suppose I tell you that a coin always comes up heads, and you proceed to toss it and it does come up heads — how much more do you believe me now?

He also has the answer worked out in detail.

(h/t Doug Burke)

From Quantile Probability and Statistical Data Modeling

by Emanuel Parzen in Statistical Science 2004, Vol 19(4), pp.652-662 JSTOR

I teach that statistics (done the quantile way) can be simultaneously frequentist and Bayesian, confidence intervals and credible intervals, parametric and nonparametric, continuous and discrete data. My first step in data modeling is identification of parametric models; if they do not fit, we provide nonparametric models for fitting and simulating the data. The practice of statistics, and the modeling (mining) of data, can be elegant and provide intellectual and sensual pleasure. Fitting distributions to data is an important industry in which statisticians are not yet vendors. We believe that unifications of statistical methods can enable us to advertise, “What is your question? Statisticians have answers!”

I couldn’t help liking this paragraph because of its bitter-sweetness. I hope you appreciate it as much as I did.

The chance that A has nukes is p%

I watched a movie in which one of the characters said, “country A has nukes with 80% chance” (perhaps, not 80% but it was a high percentage). One of the statements in that episode is that people will not eat lettuce only if the 1% chance of e coli is reported, even lower. Therefore, with such a high percentage of having nukes, it is right to send troops to A. This episode immediately brought me a thought about astronomers’ null hypothesis probability and their ways of concluding chi-square goodness of fit tests, likelihood ratio tests, or F-tests.

First of all, I’d like to ask how you would like to estimate the chance of having nukes in a country? What this 80% implies here? But, before getting to the question, I’d like to discuss computing the chance of e coli infection, first. Continue reading ‘The chance that A has nukes is p%’ »

[MADS] logistic regression

Although a bit of time has elapsed since my post space weather, saying that logistic regression is used for prediction, it looks like still true that logistic regression is rarely used in astronomy. Otherwise, it could have been used for the similar purpose not under the same statistical jargon but under the Bayesian modeling procedures. Continue reading ‘[MADS] logistic regression’ »

[Books] Bayesian Computations

A number of practical Bayesian data analysis books are available these days. Here, I’d like to introduce two that were relatively recently published. I like the fact that they are rather technical than theoretical. They have practical examples close to be related with astronomical data. They have R codes so that one can try algorithms on the fly instead of jamming probability theories. Continue reading ‘[Books] Bayesian Computations’ »


The Section on Bayesian Statistical Science (SBSS) of the American Statistical Association (ASA) would like to announce its 2010 student paper competition.  Winners of the competition will receive partial support for attending the 2010 Joint Statistical Meetings (JSM) in Vancouver, BC.


The candidate must be a member of SBSS (URL: or ISBA (International Society for Bayesian Analysis). Those candidates who have previously received travel support from SBSS are not eligible to participate. In addition, the candidate must be a full-time student (undergraduate, Masters, or Ph.D.) on or after September 1, 2009.

A manuscript, suitable for journal submission, is required for entry. The candidate must be the lead author on the paper, and hold the primary responsibility for the research and write-up.

The candidate must have separately submitted an abstract for JSM 2010 through the regular abstract submission process,  to present applied, computational, or theoretical Bayesian work. Papers should be submitted for presentation at the JSM as topic contributed or invited papers. Those papers not already a part of a session should be submitted online using the following settings:

(at URL:

* Abstract Type: Topic contributed
* Sub Type: Papers
* Sponsor: Section on Bayesian Statistical Science
* Organizer:  Alyson Wilson
* Organizer e-mail: agw -at-

Application Process

The deadline for application is Feb. 1 (same as the JSM 2010 abstract submission deadline). A formal application including the following materials should be emailed to Prof. Vanja Dukic (vanja -at-

a)      CV
b)      Abstract number (from the ASA JSM 2010 abstract submission)
c)      Letter from the major professor (advisor) or faculty co-author, verifying the student status of the candidate, and briefly describing the candidate’s role in the research and writing of the paper
d)      The manuscript, suitable for journal submission, in .pdf format.

Selection of Winners

Papers will be reviewed by a committee determined by the officers of the SBSS. Criteria for selection will include, but are not limited to, significance and potential impact of the research.  Decisions of the committee are final, and will be announced in the Spring before the JSM.


Prizes will consist of a certificate to be presented at the SBSS section meeting and partial support (up to $1000) for attending the JSM.  Please note that the awards may be unable to cover the entirety of any winner’s travel, so winning candidates may need to supplement the SBSS award with other funds. To receive a monetary prize, the winner will need to provide proof of membership and submit travel receipts to the SBSS treasurer after the JSM.

[ArXiv] Cross Validation

Statistical Resampling Methods are rather unfamiliar among astronomers. Bootstrapping can be an exception but I felt like it’s still unrepresented. Seeing an recent review paper on cross validation from [arXiv] which describes basic notions in theoretical statistics, I couldn’t resist mentioning it here. Cross validation has been used in various statistical fields such as classification, density estimation, model selection, regression, to name a few. Continue reading ‘[ArXiv] Cross Validation’ »

Robust Statistics

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’ »

systematic errors

Ah ha~ Once I questioned, “what is systematic error?” (see [Q] systematic error.) Thanks to L. Lyons’ work discussed in [ArXiv] Particle Physics, I found this paper, titled Systematic Errors describing the concept and statistical inference related to systematic errors in the field of particle physics. It, gladly, shares lots of similarity with high energy astrophysics. Continue reading ‘systematic errors’ »

[ArXiv] Particle Physics

Open Statistical Issues in Particle Physics by Louis Lyons

My recollection of meeting Prof. L. Lyons was that he is very kind and listening. I was delighted to see his introductory article about particle physics and its statistical challenges from an [arxiv:stat] email subscription. Continue reading ‘[ArXiv] Particle Physics’ »

Likelihood Ratio Technique

I wonder what Fisher, Neyman, and Pearson would say if they see “Technique” after “Likelihood Ratio” instead of “Test.” A presenter’s saying “Likelihood Ratio Technique” for source identification, I couldn’t resist checking it out not to offend founding fathers of the likelihood principle in statistics since “Technique” sounded derogatory to be attached with “Likelihood” to my ears. I thank, above all, the speaker who kindly gave me the reference about this likelihood ratio technique. Continue reading ‘Likelihood Ratio Technique’ »


We have seen the word “bipartisan” often during the election and during the on-going recession period. Sometimes, I think that the bipartisanship is not driven by politicians but it’s driven by media, commentator, and interpreters. Continue reading ‘Bipartisanship’ »

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’ »

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’ »