Sep 11th, 2009| 03:40 pm | Posted by hlee

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’ »

Tags:

book,

BUGS,

CMB,

examples,

HMM,

identifiability,

image processing,

LLN,

mixture,

MRF,

R Category:

Bayesian,

Fitting,

Languages,

MC,

MCMC,

Methods,

Stat |

1 Comment
Jul 30th, 2009| 01:57 am | Posted by hlee

##### X={ primer, tutorial, cookbook, Introduction, guidebook, 101, for dummies, …}

#####

I’ve heard many times about ** the lack of documentation** of this extensive data analysis system, **ciao**. I saw people still using ciao 3.4 although the new version 4 has been available for many months. Although **ciao** is not the only tool for Chandra data analysis, it was specifically designed for it. Therefore, I expect it being used frequently with popularity. But the reality is against my expectation. Whatever (fierce) discussion I’ve heard, it has been irrelevant to me because **ciao** is not intended for statistical analysis. Then, out of sudden, after many months, a realization hit me. **ciao is different from other data analysis systems and softwares.** This difference has been a hampering factor of introducing ciao outside the Chandra scientist community and of gaining popularity. This difference was the reason I often got lost in finding suitable documentations. Continue reading ‘Where is ciao X ?’ »

Tags:

ciao,

cookbook,

documentation,

guide,

introduction,

matlab,

primer,

Python,

R,

SAS,

software,

Tutorial Category:

Cross-Cultural,

Languages,

Misc |

1 Comment
Oct 27th, 2008| 11:05 am | Posted by hlee

The first step of data analysis or applications is reading the data sets into a tool of choice. Recent years, I’ve been using R (see also Learning R) for that regard but I’ve enjoyed freedoms for the same purpose from these languages and tools: BASIC, fortran77/90/95, C/C++, IDL, IRAF, AIPS, mongo/supermongo, MATLAB, Maple, Mathematica, SAS, SPSS, Gauss, ARC, Minitab, and recently Python and ciao which I just began to learn. Many of them I lost the fluency of how to use it. Quick learning tends to be flash memory. Some will need brain defragmentation and recovering time for extensive scientific work. A few I don’t like to use at all. No matter what, I’m not a computer geek. I’m not good at new gadgets, new softwares, nor welcome new and allegedly versatile computing systems. But one must be if he/she want to handle data. Until recently I believed R has such versatility in the aspect of reading in data. Yet, there is nothing without exceptions. Continue reading ‘read.table()’ »

Sep 18th, 2008| 07:48 pm | Posted by hlee

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’ »

Tags:

black box,

book,

catalog,

Classification,

clustering,

haste,

outliers,

R,

Robert Serfling,

semi-supervised learning,

survey Category:

Algorithms,

arXiv,

Astro,

Bad AstroStat,

Cross-Cultural,

Data Processing,

Frequentist,

Jargon,

Methods,

Stat |

Comment
Sep 16th, 2008| 04:34 pm | Posted by hlee

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’ »

Tags:

openBUGS,

PyBUGS,

Python,

R,

toolbox,

winBUGS Category:

Algorithms,

Bayesian,

Data Processing,

Languages,

MCMC,

Methods,

News |

Comment
Jun 16th, 2008| 10:47 am | Posted by hlee

As Prof. Speed said, PCA is prevalent in astronomy, particularly this week. Furthermore, a paper explicitly discusses R, a popular statistics package. Continue reading ‘[ArXiv] 2nd week, June 2008’ »

Tags:

Bayesian evidence,

Binning,

broken power law,

cosmology,

K-S test,

LF,

lhs,

likelihood,

PCA,

power spectrum,

R,

SFH,

Sun,

Tully-Fisher Category:

arXiv,

MCMC |

Comment
May 13th, 2008| 03:47 pm | Posted by hlee

The brackets could be filled with other languages but two are introduced today: **Perl** (perl.org) and **Python** (python.org). These two are widely used among astronomers and can be empowered by **R** (r-project.org). Continue reading ‘R-[{Perl,Python}] Interface’ »

Jan 29th, 2007| 11:48 am | Posted by hlee

**R** is a programming language and software for statistical computing and graphics. It is the most popular tool for statisticians and a widely used software for statistical data analysis thanks to the fact that its source code is freely available and it is fairly easy to access from installation to theoretical application.

Most of information about **R** can be found at R Project including the software itself and many add-on packages. These individually contributed packages serve particular statistical interests of their users. The documentation menu on the website and each packages contain extensive documentations of how-to’s. Some large packages include demos so that following the scripts in a demo makes R learning easy.

Continue reading ‘Learning R’ »

Jan 29th, 2007| 12:34 am | Posted by hlee

Since Summer 2005, G. Jogesh Babu (Statistics) and Eric Feigelson (Astronomy) have organized lectures and lab sessions on statistics for astronomers and physicists. Lecturers are professors from Penn State statistics department and invited renown scientists from different countries. Students show diverse demography as well. Within a week or so, students listen Statistics 101 to recently published statistical theories particularly applied to astronomical data. They also learn how to use R, a statistical software and script language to perform statistics they learn through lectures. Past two years, this summer school proved its uniqueness and usefulness. More information on the upcoming school can be found at http://astrostatistics.psu.edu/su07/index.html and other topics regarding astrostatistics at Center for AstroStatistics at Penn State.