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In this paper, we investigate the construction of compromise estimators of location and scale, by averaging over several models selected among a specified large set of possible models. The weight given to each distribution is based on the profile likelihoo ...
We analyze computational aspects of variational approximate inference techniques for sparse linear models, which have to be understood to allow for large scale applications. Gaussian covariances play a key role, whose approximation is computationally hard. ...
We present a system for estimating location and orientation of a person’s head, from depth data acquired by a low quality device. Our approach is based on discriminative random regression forests: ensembles of random trees trained by splitting each node so ...
This paper investigates the use of phoneme class conditional probabilities as features (posterior features) for template-based ASR. Using 75 words and 600 words task-independent and speaker-independent setup on Phonebook database, we investigate the use of ...
This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that contr ...
We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a pos ...
Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. ...
Class posterior distributions can be used to classify or as intermediate features, which can be further exploited in different classifiers (e.g., Gaussian Mixture Models, GMM) towards improving speech recognition performance. In this paper we examine the p ...
Milestones in sparse signal reconstruction and compressive sensing can be understood in a probabilistic Bayesian context, fusing underdetermined measurements with knowledge about low level signal properties in the posterior distribution, which is maximized ...
Institute of Electrical and Electronics Engineers2010
Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...
