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From Probability Graphical Models to Dynamic Networks — A Bayesian perspective on Smooth Best Estimate of Trajectory with applications in Geodetic Engineering

Related publications (56)

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One Step Impulse Radio Ultra-WideBand Positioning Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of positioning estimation with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths and additive Gaussian noise environments. Most popular positioning algorithms first estimate certain parameters (such as time of arr ...

IEEE2009

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of ranging with Impulse Radio (IR) Ultra-WideBand (UWB) radio under weak Line Of Sight (LOS) environments and additive Gaussian noise. We use a Bayesian approach where the prior distribution of the channel follows the IEEE 802.15.4a ...

IEEE2009

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

Alignment of Multiple Configurations Using Hierarchical Models

Yann Ruffieux

We describe a method for aligning multiple unlabeled configurations simultane- ously. Specifically, we extend the two-configuration matching approach of Green and Mardia (2006) to the multiple configuration setting. Our approach is based on the in- troduct ...

2009

A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria

Fabio Nobile

This work describes a solution to the validation challenge problem posed at the SANDIA Validation Challenge Workshop, May 21-23, 2006, NM. It presents and applies a general methodology to it. The solution entails several standard steps, namely selecting an ...

2008

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

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

Bayesian Approach for Indoor Pedestrian Localisation

The principal concept of navigation is to start from a known (initial) position and to ensure a continued and reliable localisation of the user during his/her movement. The initial position of the trajectory is usually obtained via GPS or defined by the us ...

STRC 20062006

Bayesian Approach for Indoor Pedestrian Localisation

Michel Bierlaire, Bertrand Merminod

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