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Construction and comparison of approximations for switching linear gaussian state space models

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Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures

We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multi- scale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated ...

2008

Quantifying hydrological modeling errors through a mixture of normal distributions

André Musy, Bettina Schaefli

Bayesian inference of posterior parameter distributions has become widely used in hydrological modeling to estimate the associated modeling uncertainty. The classical underlying statistical model assumes a Gaussian modeling error with zero mean and a given ...

2007

A range-only multiple target particle filter tracker

Volkan Cevher

We propose a particle filter tracker to track multiple maneuvering targets using a batch of range measurements. The state update is formulated through a locally linear motion model and the observability of the state vector is proved using geometrical argum ...

2006

A joint radar-acoustic particle filter tracker with acoustic propagation delay compensation

Volkan Cevher

In this paper, a novel particle filter tracker is presented for target tracking using collocated radar and acoustic sensors. Real-time tracking of the target's position and velocity in Cartesian coordinates is performed using batches of range and direction ...

2006

Map-matching for pedestrians via Bayesian inference

Michel Bierlaire, Bertrand Merminod

A navigation process is to start from a known (initial) position and to ensure a continued localisation of the user during the movement. Consider a pedestrian navigation system which contains a GPS receiver and a set of inertial sensors connected with the ...

2006

Construction and comparison of approximations for switching linear gaussian state space models

We consider approximate inference in a class of switching linear Gaussian State Space models which includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous hidden state. The method is a novel form of ...

IDIAP2005

Stable Directed Belief Propagation in Gaussian DAGs using the auxiliary variable trick

IDIAP2005

General state-space models

Thomas Gsponer

In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...

EPFL2004

Wavelets Versus Resels in the Context of fMRI: Establishing the Link with SPM

Michaël Unser, Dimitri Nestor Alice Van De Ville, Thierry Blu

Statistical Parametric Mapping (SPM) is a widely deployed tool for detecting and analyzing brain activity from fMRI data. One of SPM's main features is smoothing the data by a Gaussian filter to increase the SNR. The subsequent statistical inference is bas ...

SPIE2003

The Scrambled Net Filter

Thomas Gsponer

The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and smoothing in the fields of linear Gaussian state-space models. In its standard setting it has a simple recursive form which implies high computational effici ...

2002