Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 227,732,019 papers from all fields of science
Search
Sign In
Create Free Account
Metropolis–Hastings algorithm
Known as:
Metropolis method
, Metropolis sampling
, Metropolis-Hastings algorithm
Expand
In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
32 relations
Autocorrelation
Blackboard system
Computing the permanent
Detailed balance
Expand
Broader (1)
Markov chain Monte Carlo
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Review
2016
Review
2016
Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era
M. Mozer
,
Robert V. Lindsey
2016
Corpus ID: 18694048
Cognitive psychology has long had the aim of understanding mechanisms of human memory, with the expectation that such an…
Expand
2014
2014
Understanding the Hastings Algorithm
David D. L. Minh
,
Do Le Paul Minh
Communications in statistics. Simulation and…
2014
Corpus ID: 10779823
The Hastings algorithm is a key tool in computational science. While mathematically justified by detailed balance, it can be…
Expand
2013
2013
Bayesian parameter estimation for a jet-milling model using Metropolis–Hastings and Wang–Landau sampling
Catharine A. Kastner
,
A. Braumann
,
+5 authors
Chrismono Himawan
2013
Corpus ID: 53470747
2009
2009
On a Directionally Adjusted Metropolis-Hastings Algorithm
D. Fraser
2009
Corpus ID: 14639592
We propose a new Metropolis-Hastings algorithm for sampling from smooth, unimodal distributions; a restriction to the method is…
Expand
2008
2008
The Linked Importance Sampler Auxiliary Variable Metropolis Hastings Algorithm for Distributions with Intractable Normalising Constants
J. Koskinen
2008
Corpus ID: 54709739
We consider parameter inference for the class of models where the likelihoodfunction is analytically intractable as a result of a…
Expand
2005
2005
Accept – reject Metropolis – Hastings sampling and marginal likelihood estimation
John M. Olin
2005
Corpus ID: 32606348
We describe a method for estimating the marginal likelihood, based on CHIB (1995) and CHIB and JELIAZKOV (2001), when simulation…
Expand
Highly Cited
2002
Highly Cited
2002
Likelihood methods for fitting multilevel models with complex level-1 variation
W. Browne
,
D. Draper
,
J. Rasbash
,
H. Goldstein
2002
Corpus ID: 14830071
2001
2001
A novel parallel-rotation algorithm for atomistic Monte Carlo simulation of dense polymer systems
S. Santos
,
U. Suter
,
M. Müller
,
J. Nievergelt
2001
Corpus ID: 58927800
We develop and test a new elementary Monte Carlo move for use in the off-lattice simulation of polymer systems. This novel…
Expand
1998
1998
A Sequential Metropolis-hastings Algorithm
P. Vandekerkhove
1998
Corpus ID: 18799130
This paper deals with the asymptotic properties of the Metropolis-Hastings algorithm, when the distribution of interest is…
Expand
1998
1998
Solving Scheduling Problems by Simulated Annealing
O. Catoni
1998
Corpus ID: 121095240
We define a general methodology to deal with a large family of scheduling problems. We consider the case where some of the…
Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE