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Astrostatistical Challenges for the New Astronomy presents a collection of monographs authored by several of the disciplines leading astrostatisticians, i.e. by researchers from the fields of statistics and astronomy-astrophysics, who work in the statistical analysis of astronomical and cosmological data. Eight of the ten monographs are enhancements of presentations given by the authors as invited or special topics in astrostatistics papers at the ISI World Statistics Congress (2011, Dublin, Ireland). The opening chapter, by the editor, was adapted from an invited seminar given at Los Alamos National Laboratory (2011) on the history and current state of the discipline the second chapter by Thomas Loredo was adapted from his invited presentation at the Statistical Challenges in Modern Astronomy V conference (2011, Pennsylvania State University), presenting insights regarding frequentist and Bayesian methods of estimation in astrostatistical analysis. The remaining monographs are research papers discussing various topics in astrostatistics. The monographs provide the reader with an excellent overview of the current state astrostatistical research, and offer guidelines as to subjects of future research. Lead authors for each chapter respectively include Joseph M. Hilbe (Jet Propulsion Laboratory and Arizona State Univ) Thomas J. Loredo (Dept of Astronomy, Cornell Univ) Stefano Andreon (INAF-Osservatorio Astronomico di Brera, Italy) Martin Kunz ( Institute for Theoretical Physics, Univ of Geneva, Switz) Benjamin Wandel ( Institut d'Astrophysique de Paris, Univ Pierre et Marie Curie, France) Roberto Trotta (Astrophysics Group, Dept of Physics, Imperial College London, UK) Phillip Gregory (Dept of Astronomy, Univ of British Columbia, Canada) Marc Henrion (Dept of Mathematics, Imperial College, London, UK) Asis Kumar Chattopadhyay (Dept of Statistics, Univ of Calcutta, India) Marisa March (Astrophysics Group, Dept of Physics, Imperial College, London, UK).
Front Matter
Astrostatistics: A Brief History and View to the Future
Bayesian Astrostatistics: A Backward Look to the Future
Understanding Better (Some) Astronomical Data Using Bayesian Methods
BEAMS: Separating the Wheat from the Chaff in Supernova Analysis
Gaussian Random Fields in Cosmostatistics
Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm
Extra-solar Planets via Bayesian Fusion MCMC
Classification and Anomaly Detection for Astronomical Survey Data
Independent Component Analysis for Dimension Reduction Classification: Hough Transform and CASH Algorithm
Improved Cosmological Constraints from a Bayesian Hierarchical Model of Supernova Type Ia Data