Titlebook: Statistical Challenges in Modern Astronomy V; Eric D. Feigelson,G. Jogesh Babu Conference proceedings 2012 Springer Science+Business Media

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The Matter Spectral Density from Lensed Cosmic Microwave Background Observationsu and Okamoto (ApJ 557:L79–L83, 2001; ApJ 574:566–574, 2002; Phys Rev D 67:083002, 2003). We finish the paper with a discussion regarding the potential scientific applications and the challenges associated with estimating the noise spectrum from simulations.

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Robust, Data-Driven Inference in Non-linear Cosmostatisticsedshifts and their number density distribution from approximate, photometric redshift data. The second focuses on cosmic voids and uses them to construct . which allow reconstructing the expansion history of the Universe using the Alcock-Paczynski test. In both cases we find that non-linearities . t

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Simulation-Aided Inference in Cosmologynces about cosmological parameters. The first method is a Bayesian calibration approach adapted from Kennedy and O’Hagan (J R Stat Soc B 68:425–464, 2001) and Higdon et al. (J Am Stat Assoc 103:570–583, 2008). It makes use of a response surface model that approximates the simulation output at untrie

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Commentary: Simulation-Aided Inference in Cosmologyudy of a set of techniques that are likely to become a standard part of the astrostatistics toolbox. The problems addressed by these techniques models based on expensive computer simulations that run on high-performance computing (HPC) platforms, which can only sparsely sample a large-dimensional in

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