Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
Statistics and Computing
Variational Maximum A Posteriori by Annealed Mean Field Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive DOA estimation using a database of PARCOR coefficients
EURASIP Journal on Applied Signal Processing
WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
Structural break estimation of noisy sinusoidal signals
Signal Processing
Radiological source detection and localisation using Bayesian techniques
IEEE Transactions on Signal Processing
Joint estimation of source number and DOA using simulated annealing algorithm
Digital Signal Processing
IEEE Transactions on Signal Processing
Bayesian method for NLOS mitigation in single moving sensor Geo-location
Signal Processing
A Bayesian Lasso via reversible-jump MCMC
Signal Processing
Bayesian spectral estimation applied to echo signals from nonlinear ultrasound scatterers
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
A fully Bayesian model based on reversible jump MCMC and finite Beta mixtures for clustering
Expert Systems with Applications: An International Journal
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Metropolis photon sampling with optional user guidance
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
A clustering approach for estimating parameters of a profile hidden Markov model
International Journal of Data Mining and Bioinformatics
Computational system identification for Bayesian NARMAX modelling
Automatica (Journal of IFAC)
Hi-index | 35.69 |
In this paper, the problem of joint Bayesian model selection and parameter estimation for sinusoids in white Gaussian noise is addressed. An original Bayesian model is proposed that allows us to define a posterior distribution on the parameter space. All Bayesian inference is then based on this distribution. Unfortunately, a direct evaluation of this distribution and of its features, including posterior model probabilities, requires evaluation of some complicated high-dimensional integrals. We develop an efficient stochastic algorithm based on reversible jump Markov chain Monte Carlo methods to perform the Bayesian computation. A convergence result for this algorithm is established. In simulation, it appears that the performance of detection based on posterior model probabilities outperforms conventional detection schemes