Titlebook: Statistical Challenges in Astronomy; Eric D. Feigelson,G. Jogesh Babu Conference proceedings 2003 Springer Science+Business Media New York

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ed the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus eme

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Hierarchical Models, Data Augmentation, and Markov Chain Monte Carlo,ly pushing beyond the capabilities of the “classical” data-analysis methods in common use. In this chapter we discuss the use of highly structured models that not only incorporate the scientific model (e.g., for a source spectrum) but also account for stochastic components of data collection and the

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hypotension

Bayesian Model Selection and Analysis for Cepheid Star Oscillations,r absolute luminosity. Once this relationship has been calibrated, knowledge of the period gives knowledge of the luminosity. This makes these stars useful as “standard candles” for estimating distances in the universe. Available data consists of photometric and velocity information for a number of

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Statistical and Astronomical Challenges in the Sloan Digital Sky Survey, in five photometric bands, and a pair of multi-object spectrographs to measure redshifts for 10. galaxies and 10. quasars. I describe some of the recent scientific results from the survey, focusing on quasars and galaxies, with an emphasis on the statistical challenges that they raise. The data are

follicular-unit

Challenges for Cluster Analysis in a Virtual Observatory, through large digital sky surveys. Virtual Observatory (VO) concept represents a scientific and technological framework needed to cope with this data flood. We review some of the applied statistics and computing challenges posed by the analysis of large and complex data sets expected in the VO-base