The AstroStat Slog

A nice book by Christopher Bishop.
While I was reading abstracts and papers from astro-ph, I saw many applications of algorithms from pattern recognition and machine learning (PRML). The frequency will increase as large scale survey projects numerate, where recommending a good textbook or a reference in the field seems timely.

Survey and population studies generally invite large data sets. Any discussion about individual objects from that survey is an indication that those objects are outliers with respect to the objects in the catalog, created from survey and population studies. These outliers are the objects deserving strong spotlights, in contrast to the notion that outliers are useless. Other than studies about outliers, survey and population studies generally involve machine learning and pattern recognition, or supervised learning and unsupervised learning, or classification and clustering, or statistical learning. Whatever jargon you choose to use, the book overviews most popular machine learning methods extensively with examples, nice illustrations, and concise math. Upon understanding characteristics of the catalog such as dimensions, sample size, independent variable, dependent variable, missing values, sampling (volume limited, magnitude limited, incompleteness), measurement errors, scatter plots, and so on, as a second step to summarize the large data as a whole, the book could offer proper approaches based on your data analysis objective in a statistical sense – in terms of summarizing data.

Click here to access the book website for various resources including a few book chapters, retailer links, examples, and solutions. One of reviews you can check.

A lesson from reading arxiv/astro-ph during the past year is that astronomers must become interdisciplinary particularly those in surveys and creating catalogs. From the information retrieval viewpoint, some rudimentary education about pattern recognition and machine learning is a must as I personally think basic statistics and probability theory should be offered to young astronomers (like astrostatistics summer school at Penn State). While attending graduate school, I saw non stat majors taking statistics classes, except students from astronomy or physics. To confirm this hypothesis, I took computational physics to learn how astronomers and physicists handle data with uncertainty. Although it was one of my favorite classes, the course was quite off from statistics. (Game theory was the most statistically relevant subject.) Hence, I think not many astronomy departments offer practical statistics courses or machine learning and therefore, recommending good and modern textbooks related to (statistical) data analysis can be beneficial to self teaching astronomers. I hope my reasoning is in the right track.