Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders
Archive for the ‘Languages’ Category.
The full description is given http://cxc.harvard.edu/ciao3.4/ahelp/bayes.html about “bayes” under sherpa/ciao[1]. Some sentences kept bothering me and here’s my account for the reason given outside of quotes. Continue reading ‘It bothers me.’ »
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()’ »
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’ »
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’ »
Talking about limits in Numerical Recipes in my PyIMSL post, I couldn’t resist checking materials, particularly updates in the new edition of Numerical Recipes by Press, et al. (2007). Continue reading ‘NR, the 3rd edition’ »
PyIMSL is a collection of Python wrappers to the math and statistical algorithms in the IMSL C Numerical Library[1]. I recall the days of digging in IMSL (International Mathematics and Statistics Library) user manuals and learning Fortran and C to use this vast library (Splus was to slow at that time). Upon knowing that Python is very favored among astronomers (click here to see the slog posts about Python) and that limits exist in Numerical Recipes (I didn’t check the latest version published last year, though), probably IMSL is useful for mathematical and statistical analysis for astronomers.
To know more, Continue reading ‘PyIMSL’ »
I grew up in an environment that glamourized mathematical equations. Equations adorned a text like jewelry, set there to dazzle, and often to outshine the text that they were to illuminate. Needless to say, anything I wrote was dense, opaque, and didn’t communicate what it set out to. It was not until I saw a Reference Frame essay by David Mermin on how to write equations (1989, Physics Today, 42, p9) that I realized that equations should be treated as part of the text. You should be able to read them. David Mermin set out 3 rules for writing out equations, which I’ve tried to follow diligently (if not always successfully) since then. Continue reading ‘I Like Eq’ »
Diab Jerius follows up on LOESS techniques with a very nice summary update and finds LOCFIT to be very useful, but there are still questions about how it deals with measurement errors and combining observations from different experiments:
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’ »
Sherpa is a fitting environment in which Chandra data (and really, X-ray data from any observatory) can be analyzed. It has just undergone a major update and now runs on python. Or allows python to run. Something like that. It is a very powerful tool, but I can never remember how to use it, and I have an amazing knack for not finding what I need in the documentation. So here is a little cheat sheet (which I will keep updating as and when if I learn more): Continue reading ‘Everybody needs crampons’ »
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’ »
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’ »
Both in astronomy and statistics, python is recognized as a versatile programming language. I asked python tutorials to Alanna. The following is her answer, which looks very useful for those who wish to learn python.
Continue reading ‘Learning Python’ »