Bayesian Methods in Cosmology by Michael P. Hobson, Andrew H. Jaffe, Andrew R. Liddle, Pia

By Michael P. Hobson, Andrew H. Jaffe, Andrew R. Liddle, Pia Mukherjee, David Parkinson

Lately cosmologists have complex from mostly qualitative versions of the Universe to precision modelling utilizing Bayesian tools, so one can confirm the homes of the Universe to excessive accuracy. This well timed publication is the one complete creation to using Bayesian equipment in cosmological reports, and is a necessary reference for graduate scholars and researchers in cosmology, astrophysics and utilized information. the 1st a part of the booklet makes a speciality of method, atmosphere the elemental foundations and giving an in depth description of innovations. It covers subject matters together with the estimation of parameters, Bayesian version comparability, and separation of signs. the second one half explores a various diversity of functions, from the detection of astronomical assets (including via gravitational waves), to cosmic microwave heritage research and the quantification and type of galaxy houses. Contributions from 24 extremely popular cosmologists and statisticians make this an authoritative advisor to the topic.

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735, 395. Steutel, F. W. (1979). Scand. J. , 6, 57. 2 Simple applications of Bayesian methods D. S. Sivia and S. G. Rawlings Having seen how the need for rational inference leads to the Bayesian approach for data analysis, we illustrate its use with a couple of simplified cosmological examples. While real problems require analytical approximations or Monte Carlo computation for the sums to be evaluated, toy ones can be made simple enough to be done with brute force. The latter are helpful for learning the basic principles of Bayesian analysis, which can otherwise become confused with the details of the practical algorithm used to implement them.

There is no magic level at which a hypothetical model can be ‘rejected with 95% confidence’ in old-fashioned ‘orthodox’ style. All we can do is compare models (A and B, say) through their evidence ratios known as Bayes factors, BayesFactor(A, B) = EA . 82) We may feel that model A is unsatisfactory, but we are stuck with it until we can do better. We cannot stand outside our current horizon, in some mythic state of holistic totality. On the contrary, in problems of significant size we often don’t manage to fit the data well enough to satisfy an orthodox χ2 significance test.

If this is done for only one, or a few, of the coordinates, this exploration strategy is ‘Gibbs sampling’ (Gelman et al. 1995). If it is done for all coordinates, the exploration can take any direction. One of your author’s favourite tricks (Skilling 2004a) is to raster through the d-dimensional 232 -cube with a space-filling Hilbert curve, so that each point is labelled by a 32d-bit integer in a way that substantially preserves neighbourhood relationships. Exploration is then 1-dimensional, with movement coded by extended-precision addition and subtraction.

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