Statistical Challenges in Modern Astronomy IV: Proceedings of a Conference Held at Pennsylvania State University, University Park, Pennsylvania, USA, 12-15 June 2006, Volume 371"This is the fourth in a series of international conferences for the vanguard of researchers in the cross-disciplinary field of astrostatistics. Both astronomical and statistical communities now recognize the wide array of fascinating methodological issues faced by the modern astronomer. Ranging from terabyte wide-field surveys to small-N samples, from cosmology to the search for Earth-like planets, astronomical research can no longer be pursued with a small toolbox of familiar statistical methods. Over thirty distinguished scholars from both fields presented invited talks and commentaries on leading problems in astrostatistics. The methodological challenges of inferring cosmological insights from the cosmic microwave background fluctuations, the distribution of galaxies in space, gravitational lensing, and galaxy structure wre describe in detail. Time series analysis is discussed in a variety of contexts: sparse Poisson data, multiply-periodic systems, gravitational wave detection, and most dramatically in the search for extrasolar planets. Here sophisticated Bayesian model selection with MCMC computations plays a critical role. Other topics covered include image processing, analysis of mega-datasets from large surveys, and small-N problems in both astronomy and particle physics. The volume ends with cross-disciplinary overviews and software tutorials. The book will be valuable to graduate students and researchers in both astronomy and statistics who seek insights into this promising avenue of cross-disciplinary research."--Publisher's website. |
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Índice
Spatial Statistics of Cosmic Microwave Background Maps | 3 |
Setting the Statistical Significance of Detections of NonGaussianity in the WMAP | 18 |
Discussion on Baddeley Paper | 39 |
Direitos de autor | |
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algorithm analysis applied approach approximation assume astronomical Astrophysics background Bayesian calculated cluster component computational consider correlation corresponding counts curve defined density depends described detection determine discuss disk distance distribution effect energy error estimate et al example expected field Figure flux frequency function galaxy Gaussian given hypothesis important inference integral intensity interesting interval issues known lensing light likelihood limit mass matrix mean measure methods model selection Monte Carlo noise objects observed obtained orbital parameters particle pattern period photon Physics Poisson possible posterior present prior probability problem procedure question random ratio References region require sample scale selection Series shape shows signal significant simple simulations space spatial spectra standard stars statistical structure survey techniques uncertainties University variables wavelet

