The AstroStat Slog

Although a bit of time has elapsed since my post space weather, saying that logistic regression is used for prediction, it looks like still true that logistic regression is rarely used in astronomy. Otherwise, it could have been used for the similar purpose not under the same statistical jargon but under the Bayesian modeling procedures. Continue reading ‘[MADS] logistic regression’ »

A few scientists in our group work on estimating the intensities of gamma ray observations from sky surveys. This work distinguishes from typical image processing which mostly concerns the point estimation of intensity at each pixel location and the size of overall white noise type error. Often times you will notice from image processing that the orthogonality between errors and sources, and the white noise assumptions. These assumptions are typical features in image processing utilities and modules. On the other hand, CHASC scientists relate more general and broad statistical inference problems in estimating the intensity map, like intensity uncertainties at each point and the scientifically informative display of the intensity map with uncertainty according to the Poisson count model and constraints from physics and the instrument, where the field, multiscale modeling is associated. Continue reading ‘[MADS] multiscale modeling’ »

In order to understand a learning procedure statistically it is necessary to identify two important aspects: its structural model and its error model. The former is most important since it determines the function space of the approximator, thereby characterizing the class of functions or hypothesis that can be accurately approximated with it. The error model specifies the distribution of random departures of sampled data from the structural model.

Continue reading ‘A Quote on Model’ »

All models are wrong, but some are useful. –George Box

Continue reading ‘All models are wrong, but some are useful’ »

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’ »

Last week, I was at Tufts colloquium and happened to have a conversation with a computer scientist about density based clustering. I understood density as probabilistic density and was recollecting a paper by Fraley and Raftery (Model-Based Clustering, Discriminant Analysis, and Density Estimation, JASA, 2002, 97, p.458) and other similar papers I saw in engineering journals like IEEE transactions. For a few moments, I felt uncomfortable and she explained that density meant “how dense observations are.” Density based clustering was meant to be distance based clustering, like k-means, minimum spanning tree, most likely nonparametric approaches. Continue reading ‘language barrier’ »

As Alanna pointed out, astronomers and statisticians mean different things when they say “model”. To complicate matters, we have also started to use another term called “data model”. Continue reading ‘model vs model’ »