On performance potentials and conditional Monte Carlo for gradient estimation for Markov chains
type: Item
instance of: scholarly article
title: On performance potentials and conditional Monte Carlo for gradient estimation for Markov chains
zbMATH Open document ID: 0923.90031
DOI: 10.1023/A:1018985019884
author: K. Appert
published in: Annals of Operations Research
publication date: 1999-06-10T00:00:00Z
Mathematics Subject Classification ID: 91B82
zbMATH DE Number: 1299389
zbMATH Keywords: sample path-based gradient estimation
zbMATH Keywords: performance measures
zbMATH Keywords: discrete-time Markov chains
MaRDI profile type: Publication
full work available at URL: a:1018985019884
OpenAlex ID: W99729237
Recommended article: Q3130251
Recommended article: Performance gradient estimation for the very large finite Markov chains
Recommended article: Gradient estimates for the performance of Markov chains and discrete event processes
Recommended article: Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-differentiable Priors
Recommended article: Stochastic Gradient Markov Chain Monte Carlo
Recommended article: Q4026974
Recommended article: A gradient descent method for estimating the Markov chain choice model
Recommended article: Monte Carlo gradient estimation in high dimensions
Recommended article: Markov Chain Monte Carlo for Exact Inference for Diffusions
description: scientific article; zbMATH DE number 1299389