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» Speeeding Up Markov Chain Monte Carlo Algorithms
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ICPR
2008
IEEE
14 years 1 months ago
Object-of-interest extraction by integrating stochastic inference with learnt active shape sketch
This article presents a novel integrated approach to object of interest extraction, including learning to define target pattern and extracting by combining detection and segmenta...
Hongwei Li, Liang Lin, Tianfu Wu, Xiaobai Liu, Lan...
CP
2010
Springer
13 years 6 months ago
Computing the Density of States of Boolean Formulas
Abstract. In this paper we consider the problem of computing the density of states of a Boolean formula in CNF, a generalization of both MAX-SAT and model counting. Given a Boolean...
Stefano Ermon, Carla P. Gomes, Bart Selman
ICASSP
2011
IEEE
12 years 11 months ago
MCMC inference of the shape and variability of time-response signals
Signals in response to time-localized events of a common phenomenon tend to exhibit a common shape, but with variable time scale, amplitude, and delay across trials in many domain...
Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Ale...
SAC
2011
ACM
12 years 10 months ago
Parallel multivariate slice sampling
Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it requires...
Matthew M. Tibbits, Murali Haran, John C. Liechty
ECCV
2002
Springer
14 years 9 months ago
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image...
Feng Han, Zhuowen Tu, Song Chun Zhu