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The divergence of the theory and practice of vocal tract length normalization (VTLN) is addressed, with particular emphasis on the role of the Jacobian determinant. VTLN is placed in a Bayesian setting, which brings in the concept of a prior on the warping ...
We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on conte ...
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) a ...
This article presents the implementation and initial test results for an algorithm called SuffStat MCMC, which aims to speed up Approximate Bayesian Computation without likelihood. ...
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) a ...
We propose a novel fully automatic framework to detect which meeting participant is currently holding the conversational floor and when the current speaker turn is going to finish. Two sets of experiments were conducted on a large collection of multiparty ...
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. ...
This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal sequential decisions under uncertainty about the environment. Due to the uncer ...
Measures of surprise have been recently studied in statistics. This new concept can be used as the first exploratory tool to verify if a model under the null hypothesis fits appropriately. As no alternative models are necessary, the use of the measures of ...
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. ...
