# Reversible Jump MCMC Simulated Annealing for Neural Networks

arXiv: https://arxiv.org/abs/1301.3833 · HTML: https://mlsanity.com/paper/1301.3833/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2013.

## Built on by (most cited first, 3 of 3)

- [Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation](https://mlsanity.com/paper/2110.03051/) (2021)
- [Approximate blocked Gibbs sampling for Bayesian neural networks](https://mlsanity.com/paper/2208.11389/) (2022)
- [Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks](https://mlsanity.com/paper/2308.06293/) (2023)

## Abstract (arXiv)

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 search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We also show that by calibrating a Bayesian model, we can obtain the classical AIC, BIC and MDL model selection criteria within a penalized likelihood framework. Finally, we show theoretically and empirically that the algorithm converges to the modes of the full posterior distribution in an efficient way.