Concept

Posterior probability

Related publications (279)

A Novel Criterion for Classifiers Combination in Multistream Speech Recognition

In this paper we propose a novel information theoretic criterion for optimizing the linear combination of classifiers in multi stream automatic speech recognition. We discuss an objective function that achieves a trade-off between the minimization of a bou ...

2009

Saddlepoint approximation for mixture models

Anthony Christopher Davison

Two-component mixture distributions with one component a point mass and the other a continuous density may be used as priors for Bayesian inference when sparse representation of an underlying signal is required. We show how saddlepoint approximation in suc ...

2009

Predicting spacial similarity of freshwater fish biodiversity

Andrea Rinaldo, Mihaela Marian

A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we anal ...

2009

Multimodal feature extraction and fusion for audio-visual speech recognition

Mihai Gurban

Multimodal signal processing analyzes a physical phenomenon through several types of measures, or modalities. This leads to the extraction of higher-quality and more reliable information than that obtained from single-modality signals. The advantage is two ...

EPFL2009

Probability density optical tomography of confined quasiparticles in a semiconductor microcavity

Benoît Marie Joseph Deveaud, Gaël Nardin, François Morier-Genoud, Barbara Pietka, Taofiq Paraïso, Ounsi El Daïf, Roland Cerna

We present the optical tomography of the probability density of quasiparticles, the microcavity polaritons, confined in three dimensions by cylindrical traps. Collecting the photoluminescence emitted by the quasimodes under continuous nonresonant laser exc ...

2009

Near-Optimal Bayesian Localization via Incoherence and Sparsity

Volkan Cevher

This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal ...

2009

Sparse Linear Models: Variational Approximate Inference and Bayesian Experimental Design

Matthias Seeger

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian po ...

2009

Systematically heterogeneous covariance in network GEV Models

Jeffrey Newman

Mixed logit models can represent heterogeneity across individuals, in both observed and unobserved preferences, but require computationally expensive calculations to compute probabilities. A few methods for including error covariance heterogeneity in a clo ...

2009

Systematically heterogeneous covariance in network GEV models

Jeffrey Newman

2009

Preventing versus Curing: Avoiding Conflicts in Transactional Memories

Rachid Guerraoui, Aleksandar Dragojevic, Vasu Singh

Transactional memories are typically speculative and rely on contention managers to cure conflicts. This paper explores a complementary approach that prevents conflicts by scheduling transactions according to predictions on their access sets. We first expl ...

2009

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