Signal decompositions using trans-dimensional Bayesian methods, 2012. ,
URL : https://hal.archives-ouvertes.fr/tel-00765464
Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, p.1087, 1953. ,
DOI : 10.1063/1.1699114
Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970. ,
DOI : 10.1093/biomet/57.1.97
Monte Carlo Strategies in Scientific Computing, 2001. ,
DOI : 10.1007/978-0-387-76371-2
Approximating posterior distributions by mixture, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.55, issue.2, pp.409-422, 1993. ,
An Adaptive Metropolis Algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001. ,
DOI : 10.2307/3318737
Divide and Conquer: A Mixture-Based Approach to Regional Adaptation for MCMC, Journal of Computational and Graphical Statistics, vol.20, issue.1, pp.63-79, 2011. ,
DOI : 10.1198/jcgs.2010.09035
An adaptive Metropolis algorithm with online relabeling, the proceeding of the 15 th International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. ,
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995. ,
DOI : 10.1093/biomet/82.4.711
Trans-dimensional Markov chain Monte Carlo, pp.179-198, 2003. ,
Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC, IEEE Transactions on Signal Processing, vol.47, issue.10, pp.2667-2676, 1999. ,
DOI : 10.1109/78.790649
Reversible jump MCMC for joint detection and estimation of sources in colored noise, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.231-240, 2002. ,
DOI : 10.1109/78.978379
Bayesian object identification, Biometrika, vol.86, issue.3, pp.649-660, 1999. ,
DOI : 10.1093/biomet/86.3.649
Building Outline Extraction from Digital Elevation Models Using Marked Point Processes, International Journal of Computer Vision, vol.24, issue.5, pp.107-132, 2007. ,
DOI : 10.1007/s11263-005-5033-7
Bayesian deconvolution of noisy filtered point processes, IEEE Transactions on Signal Processing, vol.49, issue.1, pp.134-146, 2002. ,
DOI : 10.1109/78.890355
The Pierre Auger Project Design Report, 1997. ,
Properties and performance of the prototype instrument for the Pierre Auger Observatory, Physics Research A, vol.523, pp.50-95, 2004. ,
On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion), Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.4, pp.731-792, 1997. ,
DOI : 10.1111/1467-9868.00095
Dealing with label switching in mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.4, pp.795-809, 2000. ,
DOI : 10.1111/1467-9868.00265
Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling, Statistical Science, vol.20, issue.1, pp.50-67, 2005. ,
DOI : 10.1214/088342305000000016
Computational and Inferential Difficulties with Mixture Posterior Distributions, Journal of the American Statistical Association, vol.60, issue.451, pp.957-970, 2000. ,
DOI : 10.1080/01621459.1995.10476589
URL : https://hal.archives-ouvertes.fr/inria-00073049
Dealing with label switching under model uncertainty, " in Mixtures: estimation and applications, pp.213-239, 2011. ,
An Artificial Allocations Based Solution to the Label Switching Problem in Bayesian Analysis of Mixtures of Distributions, Journal of Computational and Graphical Statistics, vol.19, issue.2, pp.313-331, 2010. ,
DOI : 10.1198/jcgs.2010.09008
Label Switching in Bayesian Mixture Models: Deterministic Relabeling Strategies, Journal of Computational and Graphical Statistics, vol.17, issue.1, 2013. ,
DOI : 10.1007/s11222-010-9226-8
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models, Statistics and Computing, vol.62, issue.2, pp.357-366, 2010. ,
DOI : 10.1007/s11222-009-9129-8
Model based labeling for mixture models, Statistics and Computing, vol.104, issue.2, pp.1-11, 2011. ,
DOI : 10.1007/s11222-010-9226-8
Discussion of On Bayesian analysis of mixtures with an unknown number of components, Journal of the Royal Statistical Society. Series B (Statistical Methodology), vol.59, issue.4, pp.758-764, 1997. ,
Comments on “Joint Bayesian Model Selection and Estimation of Noisy Sinusoids Via Reversible Jump MCMC”, IEEE Transactions on Signal Processing, vol.61, issue.14, pp.3653-3655, 2013. ,
DOI : 10.1109/TSP.2013.2261992
Model uncertainty, Statistical Science, vol.19, issue.1, pp.81-94, 2004. ,
The SEM algorithm : a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Computational Statistics Quaterly, vol.2, pp.73-82, 1985. ,
A stochastic approximation type EM algorithm for the mixture problem, Stochastics An International Journal of Probability and Stochastic Processes, vol.41, issue.1, pp.119-134, 1992. ,
DOI : 10.1080/17442509208833797
URL : https://hal.archives-ouvertes.fr/inria-00075178
The Stochastic EM Algorithm: Estimation and Asymptotic Results, Bernoulli, vol.6, issue.3, pp.457-489, 2000. ,
DOI : 10.2307/3318671
Bayesian methods for mixture of normal distributions, 1997. ,
The Calculation of Posterior Distributions by Data Augmentation, Journal of the American Statistical Association, vol.56, issue.398, pp.528-540, 1987. ,
DOI : 10.1016/0304-4076(84)90007-1
Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.39, issue.1, pp.1-38, 1977. ,
Asymptotic properties of a stochastic EM Algorithm for estimating mixing proportions, Stochastic Models, pp.599-613, 1993. ,
DOI : 10.1214/aos/1176346060
URL : https://hal.archives-ouvertes.fr/inria-00074969
Robust clustering methods: a unified view, IEEE Transactions on Fuzzy Systems, vol.5, issue.2, pp.270-293, 1997. ,
DOI : 10.1109/91.580801
Robust and efficient estimation by minimising a density power divergence, Biometrika, vol.85, issue.3, pp.549-559, 1998. ,
DOI : 10.1093/biomet/85.3.549
Single muon response: The signal model, 2010. ,
Towards adaptive learning and inference : Applications to hyperparameter tuning and astroparticle physics, 2012. ,
URL : https://hal.archives-ouvertes.fr/tel-00773295