Stochastic simulation
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulation-based methods for blind maximum-likelihood filter identification
Signal Processing - Special issue on blind source separation and multichannel deconvolution
Digital Audio Restoration: A Statistical Model Based Approach
Digital Audio Restoration: A Statistical Model Based Approach
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
New inference strategies for solving Markov decision processes using reversible jump MCMC
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Sequential Monte Carlo EM for multivariate probit models
Computational Statistics & Data Analysis
Variational algorithms for marginal MAP
The Journal of Machine Learning Research
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Markov chain Monte Carlo (MCMC) methods, while facilitating the solution of many complex problems in Bayesian inference, are not currently well adapted to the problem of marginal maximum a posteriori (MMAP) estimation, especially when the number of parameters is large. We present here a simple and novel MCMC strategy, called State-Augmentation for Marginal Estimation (SAME), which leads to MMAP estimates for Bayesian models. We illustrate the simplicity and utility of the approach for missing data interpolation in autoregressive time series and blind deconvolution of impulsive processes.