Reversible Jump MCMC Simulated Annealing for Neural Networks
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global...
Main Authors: | Andrieu, C, de Freitas, N, Doucet, A |
---|---|
Format: | Conference item |
Izdano: |
Morgan Kaufmann
2000
|
Podobne knjige/članki
-
Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC
od: Andrieu, C, et al.
Izdano: (1999) -
New inference strategies for solving Markov Decision Processes using reversible jump MCMC
od: Hoffman, M, et al.
Izdano: (2009) -
New inference strategies for solving Markov Decision Processes using reversible jump MCMC
od: Hoffman, M, et al.
Izdano: (2009) -
Sequential MCMC for Bayesian model selection
od: Andrieu, C, et al.
Izdano: (1999) -
An Introduction to MCMC for Machine Learning
od: Andrieu, C, et al.
Izdano: (2003)