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In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...
In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...
In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...
Bayesian inference of posterior parameter distributions has become widely used in hydrological modeling to estimate the associated modeling uncertainty. The classical underlying statistical model assumes a Gaussian modeling error with zero mean and a given ...
In this paper we address an observational validation of recent theoretical results on the structure of the probability density function (pdf) of daily streamflows through the analysis of data pertaining to several catchments covering various sizes, climati ...
To aid assessments of climate change impacts on water related activities in the case study regions (CSRs) of the EC funded project SWURVE, estimates of uncertainty in climate model data need to be developed. In this paper, two methods to estimate uncertain ...
We consider an estimation procedure for discrete choice models in general and Multivariate Extreme Value (MEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and ...
We consider an estimation procedure for discrete choice models in general and Generalized Extreme Value (GEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and M ...
Microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two cond ...
We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the proba ...
