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Maximum-a-posteriori estimation with unknown regularisation parameters
2015 23rd European Signal Processing Conference (EUSIPCO), 2015This paper presents two hierarchical Bayesian methods for performing maximum-a-posteriori inference when the value of the regularisation parameter is unknown. The methods are useful for models with homogenous regularisers (i.e., prior sufficient statistics), including all norms, composite norms and compositions of norms with linear operators.
Marcelo Pereyra +2 more
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Affine invariant sparse maximum a posteriori adaptation
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012Modern speech applications utilize acoustic models with billions of parameters, and serve millions of users. Storing an acoustic model for each user is costly. We show through the use of sparse regularization, that it is possible to obtain competitive adaptation performance by changing only a small fraction of the parameters of an acoustic model.
Peder A. Olsen +3 more
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Maximum a posteriori estimation of room impulse responses
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015Estimating room impulse responses (RIRs) has a number of applications, including personalized audio, analyzing and improving acoustic behavior of concert halls, listening room compensation, sound source localization, and many others. RIRs have been estimated in essentially the same fashion for the last 50 years: Compute the cross correlation between a ...
Dinei A. F. Florêncio, Zhengyou Zhang
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Robust maximum a posteriori image super-resolution
Journal of Electronic Imaging, 2014A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution which efficiently addresses the presence of outliers in the low-resolution images is proposed. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the ...
Michalis Vrigkas +2 more
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Maximum a posteriori estimation of change points in the EEG
International Journal of Bio-Medical Computing, 1995A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels.
R, Biscay +4 more
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Maximum a Posteriori Estimation of Dynamically Changing Distributions
2009This paper presents a sequential state estimation method with arbitrary probabilistic models expressing the system's belief. Probabilistic models can be estimated by Maximum a posteriori estimators (MAP), which fail, if the state is dynamic or the model contains hidden variables.
Michael Volkhardt +3 more
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A Maximum a Posteriori Estimator for Trajectories of Diffusion Processes
Stochastics, 1987Ofer Zeitouni, A Dembo
exaly
Maximum a Posteriori Estimation of Crystallographic Phases in X-Ray Diffraction Tomography
Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2015Doğa Gürsoy +2 more
exaly