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NIPS
2008
13 years 6 months ago
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
HYBRID
2000
Springer
13 years 9 months ago
A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models
Abstract. Switching linear dynamic systems (SLDS) attempt to describe a complex nonlinear dynamic system with a succession of linear models indexed by a switching variable. Unfortu...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham
ICCV
2005
IEEE
14 years 7 months ago
On-Line Density-Based Appearance Modeling for Object Tracking
Object tracking is a challenging problems in real-time computer vision due to variations of lighting condition, pose, scale, and view-point over time. However, it is exceptionally...
Bohyung Han, Larry S. Davis
NIPS
2003
13 years 6 months ago
Sample Propagation
Rao–Blackwellization is an approximation technique for probabilistic inference that flexibly combines exact inference with sampling. It is useful in models where conditioning o...
Mark A. Paskin
JMLR
2012
11 years 7 months ago
Fast interior-point inference in high-dimensional sparse, penalized state-space models
We present an algorithm for fast posterior inference in penalized high-dimensional state-space models, suitable in the case where a few measurements are taken in each time step. W...
Eftychios A. Pnevmatikakis, Liam Paninski