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» Approximation of Discrete Phase-Type Distributions
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CVPR
2003
IEEE
14 years 9 months ago
Nonparametric Belief Propagation
In many applications of graphical models arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There...
Erik B. Sudderth, Alexander T. Ihler, William T. F...
QEST
2006
IEEE
14 years 1 months ago
Limiting Behavior of Markov Chains with Eager Attractors
We consider discrete infinite-state Markov chains which contain an eager finite attractor. A finite attractor is a finite subset of states that is eventually reached with prob...
Parosh Aziz Abdulla, Noomene Ben Henda, Richard Ma...
HYBRID
2007
Springer
14 years 1 months ago
Robust, Optimal Predictive Control of Jump Markov Linear Systems Using Particles
Hybrid discrete-continuous models, such as Jump Markov Linear Systems, are convenient tools for representing many real-world systems; in the case of fault detection, discrete jumps...
Lars Blackmore, Askar Bektassov, Masahiro Ono, Bri...
COCOA
2009
Springer
14 years 2 months ago
Matching Techniques Ride to Rescue OLED Displays
Combinatorial optimization problems have recently emerged in the design of controllers for OLED displays. The objective is to decompose an image into subframes minimizing the addre...
Andreas Karrenbauer
FOCS
2004
IEEE
13 years 11 months ago
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of) the input is specified in terms of a probability distribution. We consider the...
David B. Shmoys, Chaitanya Swamy