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2015

Inproceedings

Martino, Luca ; Elvira, Victor ; Luengo, David ; Artés-Rodríguez, Antonio ; Corander, J

Smelly Parallel MCMC Chains Inproceedings

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4070–4074, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.

Abstract | Links | BibTeX | Tags: Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization

2010

Journal Articles

Zoubir, A; Viberg, M; Yang, B; Miguez, Joaquin

Analysis of a Sequential Monte Carlo Method for Optimization in Dynamical Systems Journal Article

Signal Processing, 90 (5), pp. 1609–1622, 2010.

Abstract | Links | BibTeX | Tags: Dynamic optimization, Nonlinear dynamics, Nonlinear tracking, Sequential Monte Carlo, Stochastic optimization

2009

Inproceedings

Miguez, Joaquin ; Maiz, Cristina S; Djuric, Petar M; Crisan, Dan

Sequential Monte Carlo Optimization Using Artificial State-Space Models Inproceedings

2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 268–273, IEEE, Marco Island, FL, 2009.

Abstract | Links | BibTeX | Tags: Acceleration, Cost function, Design optimization, discrete-time dynamical system, Educational institutions, Mathematics, maximum a posteriori estimate, maximum likelihood estimation, minimisation, Monte Carlo methods, Optimization methods, Probability distribution, sequential Monte Carlo optimization, Sequential optimization, Signal design, State-space methods, state-space model, Stochastic optimization