Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference

Jump-diffusion algorithms are applied to sampling from Bayesian posterior distributions. We consider a class of random sampling algorithms based on continuous-time jump processes. The semigroup theory of random processes lets us show that limiting cases of certain jump processes acting on discretize...

Full description

Saved in:
Bibliographic Details
Main Author: Aaron Lanterman
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/13/7/1084
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849739049841983488
author Aaron Lanterman
author_facet Aaron Lanterman
author_sort Aaron Lanterman
collection DOAJ
description Jump-diffusion algorithms are applied to sampling from Bayesian posterior distributions. We consider a class of random sampling algorithms based on continuous-time jump processes. The semigroup theory of random processes lets us show that limiting cases of certain jump processes acting on discretized spaces converge to diffusion processes as the discretization is refined. One of these processes leads to the familiar Langevin diffusion equation; another leads to an entirely new diffusion equation.
format Article
id doaj-art-022a07db8c334a94aa51fc76d9b51df2
institution DOAJ
issn 2227-7390
language English
publishDate 2025-03-01
publisher MDPI AG
record_format Article
series Mathematics
spelling doaj-art-022a07db8c334a94aa51fc76d9b51df22025-08-20T03:06:24ZengMDPI AGMathematics2227-73902025-03-01137108410.3390/math13071084Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian InferenceAaron Lanterman0School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USAJump-diffusion algorithms are applied to sampling from Bayesian posterior distributions. We consider a class of random sampling algorithms based on continuous-time jump processes. The semigroup theory of random processes lets us show that limiting cases of certain jump processes acting on discretized spaces converge to diffusion processes as the discretization is refined. One of these processes leads to the familiar Langevin diffusion equation; another leads to an entirely new diffusion equation.https://www.mdpi.com/2227-7390/13/7/1084Markov chain Monte CarloMetropolis–HastingsGibbs samplingpattern theory
spellingShingle Aaron Lanterman
Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference
Mathematics
Markov chain Monte Carlo
Metropolis–Hastings
Gibbs sampling
pattern theory
title Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference
title_full Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference
title_fullStr Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference
title_full_unstemmed Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference
title_short Convergence of Limiting Cases of Continuous-Time, Discrete-Space Jump Processes to Diffusion Processes for Bayesian Inference
title_sort convergence of limiting cases of continuous time discrete space jump processes to diffusion processes for bayesian inference
topic Markov chain Monte Carlo
Metropolis–Hastings
Gibbs sampling
pattern theory
url https://www.mdpi.com/2227-7390/13/7/1084
work_keys_str_mv AT aaronlanterman convergenceoflimitingcasesofcontinuoustimediscretespacejumpprocessestodiffusionprocessesforbayesianinference