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The Multinest sampler

Nested sampling

Name: Multinest

Version: 3.7

Author(s): Farhan Feroz,Mike Hobson

URL: https://ccpforge.cse.rl.ac.uk/gf/project/multinest/

Cite: arXiv:0809.3437, arXiv:0704.3704, arXiv:1306.2144

Parallel: parallel

Nested sampling is a method designed to calculate the Bayesian Evidence of a distribution, for use in comparing multiple models to see which fit the data better.

The evidence is the integral of the likelihood over the prior; it is equivalent to the probability of the model given the data (marginalizing over the specific parameter values): B = P(D|M) = \int P(D|Mp) P(p|M) dp

Nested sampling is an efficient method for evaluating this integral using members of an ensemble of live points and steadily replacing the lowest likelihood point with a new one from a gradually shrinking proposal so and evaluating the integral in horizontal slices.

Multinest is a particularly sophisticated implementation of this which can cope with multi-modal distributions using a k-means clustering algorithm and a proposal made from a collection of ellipsoids.

The output from multinest is not a set of posterior samples, but rather a set of weighted samples - when making histograms or parameter estimates these must be included.

The primary mulitnest parameter is the number of live points in the ensemble. If this number is too small you will get too few posterior samples in the result, and if it is too large the sampling will take a long time. A few hundred seems to be reasonable for typical cosmology problems.

Installation

No special installation required; everything is packaged with CosmoSIS

Parameters

These parameters can be set in the sampler's section in the ini parameter file.
If no default is specified then the parameter is required. A listing of "(empty)" means a blank string is the default.

Parameter Type Meaning Default
live_points integer Number of live points in the ensemble
random_seed integer Seed to use for random proposal; -1 to generate from current time. Allows re-running chains exactly -1
feedback bool Print out progression information from multinest T
resume bool If you previously set multinest_outfile_root you can restart an interrupted chain with this setting F
multinest_outfile_root str In addition to CosmoSIS output, save a collection of multinest output files (empty)
ins boolean Use Importance Nested Sampling (INS) mode - see papers for more info True
efficiency float Target efficiency for INS - see papers 0.1
update_interval integer Frequency of printed output from inside multinest 200
max_iterations integer Maximum number of samples to take
mode_ztolerance float If multi-modal, get separate stats for modes with this evidence difference 0.5
log_zero float Log-probabilities lower than this value are considered to be -infinity -1e5
cluster_dimensions integer Look for multiple modes only on the first dimensions -1
max_modes integer If multi-modal, maximum number of allowed modes 100
mode_separation bool Optimize for multi-modal or other odd likelihoods - split into different proposal modes N
constant_efficiency bool Constant efficiency mode - see papers N
tolerance float Target error on evidence 0.1

Updated