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Discrete-Choice Mining of Social Processes

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A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding

Michaël Unser, Thierry Blu, Florian Luisier

This paper introduces a new approach to orthonormal wavelet image denoising. Instead of postulating a statistical model for the wavelet coefficients, we directly parametrize the denoising process as a sum of elementary nonlinear processes with unknown weig ...

Institute of Electrical and Electronics Engineers2007

Gaussian Process Belief Propagation

Matthias Seeger

The framework of graphical models is a cornerstone of applied Statistics, allowing for an intuitive graphical specification of the main features of a model, and providing a basis for general Bayesian inference computations though belief propagation (BP). I ...

MIT Press2007

Mode and Carrier Choice in the Quebec City - Windsor Corridor: A Random Parameters Approach

The Quebec City-Windsor corridor is the busiest and most important trade and transportation corridor in Canada. The transportation sector is the second largest contributor to greenhouse gas (GHG) emissions in the country. Governments around the world, incl ...

2007

Mode and Carrier Choice in the Quebec City - A Random Parameters Approach

2007

Mode and Carrier Choice in the Quebec City - Windsor Corridor: A Random Parameters Approach

2007

Dynamical Dirichlet Mixture Model

Jean-Marc Odobez, Le Chen

In this report, we propose a statistical model to deal with the discrete-distribution data varying over time. The proposed model -- HMM+DM -- extends the Dirichlet mixture model to the dynamic case: Hidden Markov Model with Dirichlet mixture output. Both t ...

IDIAP2007

Implementing Belief Propagation on P-Grid

Nicolas Bonvin, Damien Auroux, Grégoire Montavon

Some applications require to solve a Bayesian network in a distributed fashion. It can lead to a large amount of messages exchanged between hosts if the variables of the Bayesian network are not allocated to good hosts. The technique we implement uses an a ...

2007

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...

2006

Worst-Case Bounds for Gaussian Process Models

Matthias Seeger

We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over ...

2006

Influence of lamination parameters on LTCC shrinkage under unconstrained sintering

Peter Ryser, Thomas Maeder, Yannick Sébastien Fournier, Louis-Séverin Bieri

LTCC (Low-Temperature Cofired Ceramic) has attracted considerable interest as a material for sensors and microfluidic circuits, for which dimensional accuracy is essential. The irregular shrinkage behaviour of LTCC tapes must be taken into account when des ...

2006