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 a...
Serena Soldo, Mathew Magimai-Doss, Joel Pinto, Her...
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Recent advances in statistical machine translation have used approximate beam search for NP-complete inference within probabilistic translation models. We present an alternative ap...
Abhishek Arun, Barry Haddow, Philipp Koehn, Adam L...
Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that la...
Background: Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variat...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...
Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image detection, the a priori dist...
The aim of this work is to investigate how to exploit the temporal information in a video sequence for the task of face recognition. Following the approach in [11], we propose a p...
Abstract. We present a Bayesian approach for simultaneously estimating the number of people in a crowd and their spatial locations by sampling from a posterior distribution over cr...
— We consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are not known exactly. Partially Observable Mark...