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NestedSampling: A port of John Skilling's nested sampling C code to Haskell.

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Nested Sampling is a numerical algorithm for approximate Bayesian inference. It generates samples from the posterior distribution but its main purpose is to estimate the evidence P(M|D) of the model conditioned on the observed data. More information on Nested Sampling is available at http://en.wikipedia.org/wiki/Nested_sampling_algorithm.

The original code can be found at http://www.inference.phy.cam.ac.uk/bayesys/sivia/ along with documentation at http://www.inference.phy.cam.ac.uk/bayesys/. An example program called lighthouse.hs is included.

So far, only the simple demonstration file called mininest.c has been ported. There is a more sophisticated C library available at http://www.inference.phy.cam.ac.uk/bayesys/nest/nest.tar.gz but it has not been ported to Haskell yet.

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Versions [RSS] 0.1.1, 0.1.2, 0.1.3, 0.1.4
Dependencies base (>=4 && <5), random, vector [details]
License GPL-2.0-only
Copyright (C) Sivia, Skilling 2006, Trotts 2011
Author Issac Trotts
Maintainer [email protected]
Category Statistics
Home page https://github.com/ijt/haskell_nested_sampling
Source repo head: git clone git://github.com/ijt/haskell_nested_sampling.git
Uploaded by IssacTrotts at 2011-09-22T07:24:27Z
Distributions NixOS:0.1.4
Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 3849 total (3 in the last 30 days)
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Status Docs uploaded by user
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Readme for NestedSampling-0.1.4

[back to package description] 
The code here is a fairly straightforward translation of the tutorial
nested sampling code from Skilling and Sivia. The translation was
done by Issac Trotts starting in June 2011.

What follows is an adaptation of the original README:

This directory holds little toy nested-sampling programs
in Haskell, adapted from the C code in the update of Devinder's book "Data
Analysis: a Bayesian Tutorial" (2nd edition) OUP 2006.

To get started, install Haskell (GHC), then run
$ cabal install

Try out the example program like this:

$ lighthouse 
logZ: -160.48 +- 0.17
information: 2.90 nats
1000 samples

x = 1.25 +- 0.18
y = 1.00 +- 0.20

Details can be found at the top of lighthouse.hs.
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