The AstroStat Slog

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’ »

Since I learned Hubble’s tuning fork^[1] for the first time, I wanted to do classification (semi-supervised learning seems more suitable) galaxies based on their features (colors and spectra), instead of labor intensive human eye classification. Ironically, at that time I didn’t know there is a field of computer science called machine learning nor statistics which do such studies. Upon switching to statistics with a hope of understanding statistical packages implemented in IRAF and IDL, and learning better the contents of Numerical Recipes and Bevington’s book, the ignorance was not the enemy, but the accessibility of data was. Continue reading ‘[ArXiv] 5th week, Apr. 2008’ »

1. Wikipedia link: Hubble sequence[↩]

The dichotomy of outliers; detecting outliers to be discarded or to be investigated; statistics that is robust enough not to be influenced by outliers or sensitive enough to alert the anomaly in the data distribution. Although not related, one paper about outliers made me to dwell on what outliers are. This week topics are diverse. Continue reading ‘[ArXiv] 3rd week, Apr. 2008’ »

I’m very curious how astronomers began to use Monte Carlo Markov Chain instead of Markov chain Monte Carlo. The more it becomes popular, the more frequently Monte Carlo Markov Chain appears. Anyway, this week, I added non astrostatistical papers in the list: a tutorial, big bang, and biblical theology. Continue reading ‘[ArXiv] 1st week, Apr. 2008’ »