 STATISTICS SEMINAR
 Speaker: Mr Barrie Stokes, School of Medicine and Public Health, The University of Newcastle
 Title: Nested Sampling: How it Works and why it’s Good
 Location: Room V101, Mathematics Building (Callaghan Campus) The University of Newcastle
 Time and Date: 2:00 pm, Fri, 6^{th} Jun 2014
 PhD Confirmation
 Abstract:
[Supervisors: Professor Irene Hudson, Dr. Frank Tuyl, School of Mathematical and Physical Sciences]
Nested Sampling (NS) is a computationallyintensive algorithm for fitting parametric statistical
models to data in a Bayesian setting, first announced by its originator, John Skilling, in 2004.
NS has several distinguishing features that differentiate it from the large class of algorithms
having the same purpose that fall under the heading MCMC (Markov Chain Monte Carlo).
NS has as its principal aim the evaluation of the evidence Z, the denominator in the Bayes’
Theorem expression for the posterior probability. Posterior samples are in a sense byproducts of
the process. NS requires no burnin period, and poses no starting point problem. In principle it can
deal with multimodal likelihoods and very large datasets.
The NS algorithm will be explained with the aid of Mathematica animated graphics, and some
current applications of NS will be briefly mentioned.
One of the aims of the project is to produce a general NS package written in Mathematica, which
will be used for investigating the behaviour of NS. The package should be useful in furthering the
use of NS in a variety of applications.
All algorithm development, testing, and implementation, and thesis writing is being done in Mathematica.
Reasons for this will be offered.
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