Nested sampling for Potts models

Iain Murray; David J. C. MacKay; Zoubin Ghahramani; John Skilling

Nested sampling for Potts models

Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Nested sampling is a new Monte Carlo method by Skilling intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 18
Editors	Y. Weiss, B. Schölkopf, J. Platt
Place of Publication	Cambridge, MA
Publisher	MIT Press
Pages	947-954
Number of pages	8
Publication status	Published - 2006

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http://books.nips.cc/papers/files/nips18/NIPS2005_0721.pdf

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title = "Nested sampling for Potts models",

abstract = "Nested sampling is a new Monte Carlo method by Skilling intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model.",

author = "Iain Murray and MacKay, \{David J. C.\} and Zoubin Ghahramani and John Skilling",

year = "2006",

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publisher = "MIT Press",

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AU - Murray, Iain

AU - MacKay, David J. C.

AU - Ghahramani, Zoubin

AU - Skilling, John

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Y1 - 2006

N2 - Nested sampling is a new Monte Carlo method by Skilling intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model.

AB - Nested sampling is a new Monte Carlo method by Skilling intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model.

M3 - Conference contribution

SP - 947

EP - 954

BT - Advances in Neural Information Processing Systems 18

A2 - Weiss, Y.

A2 - Schölkopf, B.

A2 - Platt, J.

PB - MIT Press

CY - Cambridge, MA

ER -

Nested sampling for Potts models

Abstract

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