Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
: A Bayesian distributed online change detection algorithm is proposed for monitoring a dynamical system by a wireless sensor network. The proposed solution relies on modelling the...
In this paper, we propose a method for 3D-model retrieval from one or more photos. This method provides an ”optimal” selection of 2D views to represent a 3D-model, and a proba...
This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution mod...
The online learning problem requires a player to iteratively choose an action in an unknown and changing environment. In the standard setting of this problem, the player has to ch...