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Bayesian Modelling in Machine Learning: A Tutorial Review

Publications associées (33)

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Embeddings of maximal tori in classical groups over local and global fields

Eva Bayer Fluckiger, Ting-Yu Lee

Embeddings of maximal tori in classical groups over fields of characteristic not 2 are the subject matter of several recent papers. The aim of the present paper is to give necessary and sufficient conditions for such an embedding to exist, when the base fi ...

Turpion Ltd2016

A Bayesian view of doubly robust causal inference

In causal inference the effect of confounding may be controlled using regression adjustment in an outcome model, propensity score adjustment, inverse probability of treatment weighting or a combination of these. Approaches based on modelling the treatment ...

Oxford University Press2016

Combine and Conquer

Vincent Etter

In this thesis, we explore the application of data mining and machine learning techniques to several practical problems. These problems have roots in various fields such as social science, economics, and political science. We show that computer science tec ...

EPFL2015

Objective Bayesian Model Selection

Thomas Lugrin

Frequentist and Bayesian approaches to statistics have long been seen as incompatible, but recent work has been done to try and unify them (Bayarri and Berger, 2004; Efron, 2005). Empirical Bayes, approximate Bayesian analysis, and the matching prior appro ...

2015

Standards for dielectric elastomer transducers

Herbert Shea, Samuel Rosset, Claire Jean-Mistral

Dielectric elastomer transducers consist of thin electrically insulating elastomeric membranes coated on both sides with compliant electrodes. They are a promising electromechanically active polymer technology that may be used for actuators, strain sensors ...

Institute of Physics2015

Sparsity and the Bayesian perspective

Jean-Luc Starck, Anaïs Marie Rassat

Sparsity has recently been introduced in cosmology for weak-lensing and cosmic microwave background (CMB) data analysis for different applications such as denoising, component separation, or inpainting (i.e., filling the missing data or the mask). Although ...

Edp Sciences S A2013

Great expectations for photoelectrochemical water splitting

Michael Graetzel, Stanley David Tilley

The first Bilateral Energy Conference-organized jointly by the E-MRS and MRS-was held at the E-MRS Spring Meeting this year and included the first symposium to appear in Europe dedicated to solar hydrogen production. The strong turnout and high level of in ...

Elsevier Science Bv2012

Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models

Matthias Seeger

Probabilistic matrix factorization methods aim to extract meaningful correlation structure from an incomplete data matrix by postulating low rank constraints. Recently, variational Bayesian (VB) inference techniques have successfully been applied to such l ...

2012

Methods for Single Trial Analysis of Asynchronous EEG Patterns

Nicolas Bourdaud

Processing of electroencephalographic (EEG) signals has mostly focused on analysing correlates that are time-locked to an observable event. However, when the signal is acquired in less controlled environment, like in the context of a brain-computer interfa ...

EPFL2012

A New Hybrid Bayesian-Variational Particle Filter With Application To Mitotic Cell Tracking

Michaël Unser, Ricard Delgado Gonzalo

Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, ...

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