Paper Info
Title
Genetic Monte Carlo Markov Chains.
Abstract
Bayesian Neural Networks - considering priors and averaging model results accordingly with weights probabilities - can be an important resource in solving classification problems whose learning sets have few samples. Hybrid Monte Carlo Markov Chains (HMCMC) are typically used to numerically solve the integrals involved in learning procedures; in this work a Genetic Algorithm is proposed as alternative to gradient measure to hybridize MCMC so that multimodal distribution can be better fitted and derivative calculation needed for gradient information can be omitted.
Year
DOI
Venue
2004
10.1007/1-4020-3432-6_30
Biological and Artificial Intelligence Environments
Keywords
Field
DocType
Bayesian Neural Networks,Genetic Algorithms,Monte Carlo Markov Chains,Metropolis Algorithm
Markov chain mixing time,Monte Carlo method,Markov chain Monte Carlo,Pattern recognition,Metropolis–Hastings algorithm,Computer science,Markov chain,Hybrid Monte Carlo,Algorithm,Parallel tempering,Artificial intelligence,Monte Carlo molecular modeling
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
1
Name
Order
Citations
PageRank
Stefano Hajek101.01