#### [Book] The Elements of Statistical Learning, 2nd Ed.

__This was written more than a year ago, and I forgot to post it.__

Continue reading ‘[Book] The Elements of Statistical Learning, 2nd Ed.’ »

Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders

Archive for the ‘Algorithms’ Category.

__This was written more than a year ago, and I forgot to post it.__

Continue reading ‘[Book] The Elements of Statistical Learning, 2nd Ed.’ »

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’ »

I often feel irksome whenever I see a function being normalized over a feasible parameter space and it being used as a probability density function (pdf) for further statistical inference. In order to be a suitable pdf, normalization has to be done over a measurable space not over a feasible space. Such practice often yields biased best fits (biased estimators) and improper error bars. On the other hand, validating a measurable space under physics seems complicated. To be precise, we often lost in translation. Continue reading ‘A short note on Probability for astronomers’ »

I was told to stay away from python and I’ve obeyed the order sincerely. However, I collected the following stuffs several months back at the instance of hearing about import inference and I hate to see them getting obsolete. At that time, collecting these modules and getting through them could help me complete the first step toward the quest Learning Python (the first posting of this slog). Continue reading ‘some python modules’ »

As a part of exploring spatial distribution of particles/objects, not to approximate via Poisson process or Gaussian process (parametric), nor to impose hypotheses such as homogenous, isotropic, or uniform, various **nonparametric** methods somewhat dragged my attention for data exploration and preliminary analysis. Among various nonparametric methods, the one that I fell in love with is tessellation (state space approaches are excluded here). Computational speed wise, I believe tessellation is faster than kernel density estimation to estimate level sets for multivariate data. Furthermore, conceptually constructing polygons from tessellation is intuitively simple. However, coding and improving algorithms is beyond statistical research (check books titled or key-worded partially by **computational geometry**). Good news is that for computation and getting results, there are some freely available softwares, packages, and modules in various forms. Continue reading ‘[ArXiv] Voronoi Tessellations’ »

[arXiv:stat.ME:0910.2585]

Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications

byMurphy, Dean, and Raftery

Classifying or clustering (or semi supervised learning) spectra is a very challenging problem from collecting statistical-analysis-ready data to reducing the dimensionality without sacrificing complex information in each spectrum. Not only how to estimate spiky (not differentiable) curves via statistically well defined procedures of estimating equations but also how to transform data that match the regularity conditions in statistics is challenging.

Continue reading ‘[ArXiv] classifying spectra’ »

From SINGS (Spitzer Infrared Nearby Galaxies Survey): Isn’t it a beautiful Hubble tuning fork? Continue reading ‘SINGS’ »

Thanks to a Korean solar physicist^{[1]} I was able to gather the following websites and some relevant information on Space Weather Forecast in action, not limited to literature nor toy data.

- Space Weather Research Lab at NJIT
- SEEDS — Solar Eruptive Event Detection System at George Mason University.
- CACTUS A software package for ‘Computer Aided CME Tracking
- SRON in the Netherlands

Continue reading ‘More on Space Weather’ »

- I must acknowledge him for his kindness and patience. He was my wikipedia to questions while I was studying the Sun.[↩]

Soon it’ll not be qualified for [MADS] because I saw some abstracts with the phrase, **compressed sensing** from arxiv.org. Nonetheless, there’s one publication within refereed articles from ADS, so far.

http://adsabs.harvard.edu/abs/2009MNRAS.395.1733W.

Title:Compressed sensing imaging techniques for radio interferometry

Authors:Wiaux, Y. et al. Continue reading ‘[MADS] compressed sensing’ »

I happened to observe a surge of principle component analysis (PCA) and independent component analysis (ICA) applications in astronomy. The PCA and ICA is used for separating mixed components with some assumptions. For the PCA, the decomposition happens by the assumption that original sources are orthogonal (uncorrelated) and mixed observations are approximated by multivariate normal distribution. For ICA, the assumptions is sources are independent and not gaussian (it grants one source component to be gaussian, though). Such assumptions allow to set dissimilarity measures and algorithms work toward maximize them. Continue reading ‘[ArXiv] component separation methods’ »

Speaking of XAtlas from my previous post I tried another visualization tool called **Parallel Coordinates** on these Capella observations and two stars with multiple observations (AL Lac and IM Peg). As discussed in [MADS] Chernoff face, full description of the catalog is found from XAtlas website. The reason for choosing these stars is that among low mass stars, next to Capella (I showed 16), IM PEG (HD 21648, 8 times), and AR Lac (although different phases, 6 times) are most frequently observed. I was curious about which variation, within (statistical variation) and between (Capella, IM Peg, AL Lac), is dominant. How would they look like from the parametric space of High Resolution Grating Spectroscopy from Chandra? Continue reading ‘[MADS] Parallel Coordinates’ »

A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution

Vonesch and Unser (2008)

IEEE Trans. Image Proc. vol. 17(4), pp. 539-549

Quoting the authors, I also like to say that __the recovery of the original image from the observed is an ill-posed problem__. They traced the efforts of wavelet regularization in deconvolution back to a few relatively recent publications by astronomers. Therefore, I guess the topic and algorithm of this paper could drag some attentions from astronomers. Continue reading ‘Wavelet-regularized image deconvolution’ »

Please, do not confuse **adaptive filter** (hereafter, **AF**) with **adaptive optics** (hereafter, **AO**). I have no expertise in both fields but have small experiences to tell you the difference. Simply put, **AF** is comparable to software as opposed to **AO** to hardware, which is for constructing telescopes in order to collect data with sharpness and to minimize time varying atmospheric blurring. When you search **adaptive filter** in ADS you’ll more likely come across with **adaptive optics** and **notch filter**. Continue reading ‘[MADS] Adaptive filter’ »

How would you assign orders to multivariate data? If you have your strategy to achieve this ordering task, I’d like to ask, “is your strategy **affine invariant**?” meaning that shift and rotation invariant. Continue reading ‘[MADS] data depth’ »

This simple law, despite my trial of full text search, was not showing in ADS. As discussed in systematic errors, astronomers, like physicists, show their error components in two additive terms; statistical error + systematic error. To explain such decomposition and to make error analysis statistically rigorous, the **law of total variance (LTV)** seems indispensable. Continue reading ‘[MADS] Law of Total Variance’ »