Archive for September 2009

To Become a Good Astronomer

By accident, a piece of paper was found from my old text book. I have no idea who wrote this, nor how old it is. Too old to be obsolete? But it has general description to become a good person and scientist Continue reading ‘To Become a Good Astronomer’ »

More on Space Weather

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.

Continue reading ‘More on Space Weather’ »

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

[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’ »

[MADS] compressed sensing

Soon it’ll not be qualified for [MADS] because I saw some abstracts with the phrase, compressed sensing from Nonetheless, there’s one publication within refereed articles from ADS, so far.
Title:Compressed sensing imaging techniques for radio interferometry
Authors: Wiaux, Y. et al. Continue reading ‘[MADS] compressed sensing’ »

[ArXiv] component separation methods

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’ »


ARCH (autoregressive conditional heteroscedasticity) is a statistical model that considers the variance of the current error term to be a function of the variances of the previous time periods’ error terms. I heard that this model made Prof. Engle a Nobel prize recipient. Continue reading ‘[MADS] ARCH’ »

[ArXiv] Statistical Analysis of fMRI Data

[arxiv:0906.3662] The Statistical Analysis of fMRI Data by Martin A. Lindquist
Statistical Science, Vol. 23(4), pp. 439-464

This review paper offers some information and guidance of statistical image analysis for fMRI data that can be expanded to astronomical image data. I think that fMRI data contain similar challenges of astronomical images. As Lindquist said, collaboration helps to find shortcuts. I hope that introducing this paper helps further networking and collaboration between statisticians and astronomers.

List of similarities Continue reading ‘[ArXiv] Statistical Analysis of fMRI Data’ »