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Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Publications associées (33)

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Simple Methods for Evaluating and Comparing Binary Experiments

Thomas Alois Weber

We consider a confidence parametrization of binary information sources in terms of appropriate likelihood ratios. This parametrization is used for Bayesian belief updates and for the equivalent comparison of binary experiments. In contrast to the standard ...

2010

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

Convex Variational Bayesian Inference for Large Scale Generalized Linear Models

Matthias Seeger

We show how variational Bayesian inference can be implemented for very large binary classification generalized linear models. Our relaxation is shown to be a convex problem for any log-concave model, and we provide an efficient double loop algorithm for so ...

2009

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger

The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...

Massachusetts Institute of Technology Press2008

Compressed Sensing and Bayesian Experimental Design

Matthias Seeger

We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study about linearly measuring natural images, we show that the si ...

2008

Bayesian Inference for Sparse Generalized Linear Models

Matthias Seeger, Matthias Bethge

We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties ...

Springer2007

The purpose of this thesis is to investigate methods for the solution of multiscale problems both from the mathematical and numerical point of view, with a particular concern on applications to flows through heterogeneous porous media. After an overview of ...

EPFL2007

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger

The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian infer ...

2007

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models

Silvia Chiappa

Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to these models is an attractive approach, used successfully in application ...

2006

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models

Silvia Chiappa

IDIAP2006