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Linking Objects in Videos by Importance Sampling

Publications associées (34)

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Importance sampling for activity path choice

Michel Bierlaire, Antonin Danalet

We propose a model for the choice of an activity pattern. Models of activity participation patterns allow to assess the impact of demand management strategies on activity and destination choices. In particular, we focus on choice set generation of activity ...

2015

Importance sampling for activity path choice

Michel Bierlaire, Antonin Danalet

2015

ABC Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prior distribution on a class of simulators (generative mode ...

2013

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

We consider the previously unsolved problem of sampling paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and he ...

2013

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

2011

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

2011

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

We consider the previously unsolved problem of sampling cycle-free paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all p ...

2011

Learning in Gaussian Markov random fields

Thomas Riedl

This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information o ...

2010

Combining Geometric and Appearance Priors for Robust Homography Estimation

Pascal Fua, Vincent Lepetit, Mustafa Özuysal

The homography between pairs of images are typically computed from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter ...

2010

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