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Modeling of Convolutive Audio Mixtures Applied to Source Separation
We consider the task of under-determined and determined reverberant audio source separation, that is the extraction of the signal of each source from a multichannel... -
Selecting Hidden Markov Chain States Number with Cross-Validated Likelihood
The problem of estimating the number of hidden states in a hidden Markov chain model is considered. Emphasis is placed on cross-validated likelihood criteria. Using... -
Learning Switching Dynamic Models for Objects Tracking
Many recent tracking algorithms rely on model learning methods. A promising approach consists of modelling the object motion with switching autoregressive models. This... -
An Expectation-Maximization algorithm for the Wishart mixture model. Applicat...
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EM-based image segmentation using Potts models with external field
Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, a simple... -
Strategies for Getting the Highest Likelihood in Mixture Models
We compare simple strategies to get maximum likelihood parameter estimation in mixture models when using the EM algorithm. All considered strategies are aiming to... -
Adaptive pixon represented segmentation (APRS) for 3D MR brain images based o...
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Rigid Point-Surface Registration using Oriented Points and an EM Variant of I...
We investigate in this research report the rigid registration of a set of points with a surface for computer-guided oral implants surgery. We first formulate the... -
Convergence of MCEM and Related Algorithms for Hidden Markov Random Field
The Monte Carlo EM (MCEM) algorithm is a stochastic version of the EM algorithm using MCMC methods to approximate the conditional distribution of the hidden data. In... -
EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Ima...
This paper deals with Markov random field model-based image segmentation. This involves parameter estimation in hidden Markov models for which one of the most widely... -
A Component-Wise EM Algorithm for Mixtures
In some situations, EM algorithm shows slow convergence problems. One possible reason is that standard procedures update the parameters simultaneous- ly. In this paper... -
A Deterministic Annealing PMHT Algorithm with an Application to Particle Trac...
We introduce the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed... -
EM Algorithms for Probabilistic Mapping Networks
The Expectation-Maximization (EM) algorithm is a general technique for maximum likelihood estimation (MLE). In this paper we present several of the important... -
A generative model for rank data based on an insertion sorting algorithm
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Adaptive mixtures of regressions: Improving predictive inference when populat...
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Model-based clustering for multivariate functional data
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Bayesian Markov model for cooperative clustering: application to robust MRI b...
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Fast joint detection-estimation of evoked brain activity in event-related fMR...
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Gaussian Parsimonious Clustering Models Scale Invariant and Stable by Projection
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Partially supervised Independent Factor Analysis using soft labels elicited f...
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