The Metropolis-Hastings algorithm
pith:KFCUIQE2 Add to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{KFCUIQE2}
Prints a linked pith:KFCUIQE2 badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original abstract
This short note is a self-contained and basic introduction to the Metropolis-Hastings algorithm, this ubiquitous tool used for producing dependent simulations from an arbitrary distribution. The document illustrates the principles of the methodology on simple examples with R codes and provides references to the recent extensions of the method.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Introduces a Bayesian framework viewing LLM prompts as textual parameters and proposes MHLP, a novel MCMC algorithm using LLM proposals, to perform inference and improve accuracy plus uncertainty quantification on benchmarks.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.