Methods › General › Markov Chain Monte Carlo › Metropolis Hastings

Metropolis Hastings

10 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Metropolis-Hastings is a Markov Chain Monte Carlo (MCMC) algorithm for approximate inference. It allows for sampling from a probability distribution where direct sampling is difficult - usually owing to the presence of an intractable integral.

M-H consists of a proposal distribution q(θ^′|θ) to draw a parameter value. To decide whether θ^′ is accepted or rejected, we then calculate a ratio:

(p(θ^′|D))/(p(θ|D))

We then draw a random number r ∈[0, 1] and accept if it is under the ratio, reject otherwise. If we accept, we set θᵢ = θ^′ and repeat.

By the end we have a sample of θ values that we can use to form quantities over an approximate posterior, such as the expectation and uncertainty bounds. In practice, we typically have a period of tuning to achieve an acceptable acceptance ratio for the algorithm, as well as a warmup period to reduce bias towards initialization values.

Image: Samuel Hudec

Papers archive 2025-07-28

10 shown of 10, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
29 Jan 2024 · 0 repositories · arXiv:2401.15948
Binary classification based Monte Carlo simulation
29 Jul 2023 · 0 repositories · arXiv:2307.16035
Data Subsampling for Bayesian Neural Networks
17 Oct 2022 · 1 repository · arXiv:2210.09141
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection
2 Sep 2022 · 0 repositories · arXiv:2209.01269
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models
16 Nov 2021 · 0 repositories
Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss
24 Sep 2019 · 1 repository · arXiv:1909.10670

Syntology ran 0 of 1 samples · 1 unverified
Hawkes Processes with Stochastic Excitations
22 Sep 2016 · 0 repositories · arXiv:1609.06831
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
7 Sep 2015 · 0 repositories · arXiv:1509.02151
Neural Adaptive Sequential Monte Carlo
10 Jun 2015 · 0 repositories · arXiv:1506.03338
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
13 Jan 2014 · 0 repositories · arXiv:1401.2838

Tasks archive 2025-07-28

15 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Attribute	1
Bayesian Inference	1
Binary Classification	1
Classification	1
Density Ratio Estimation	1
Federated Learning	1
Image Generation	1
Language Modeling	1
Language Modelling	1
Model Selection	1
Normalising Flows	1
Probabilistic Programming	1
Style Transfer	1
Text Generation	1
Variational Inference	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (10 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Markov Chain Monte Carlo

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