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CSDA
2010
118views more  CSDA 2010»
13 years 10 months ago
Grapham: Graphical models with adaptive random walk Metropolis algorithms
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementat...
Matti Vihola
STOC
1997
ACM
125views Algorithms» more  STOC 1997»
14 years 1 months ago
An Interruptible Algorithm for Perfect Sampling via Markov Chains
For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an...
James Allen Fill
AAAI
2006
13 years 11 months ago
Probabilistic Self-Localization for Sensor Networks
This paper describes a technique for the probabilistic self-localization of a sensor network based on noisy inter-sensor range data. Our method is based on a number of parallel in...
Dimitri Marinakis, Gregory Dudek
CVPR
2007
IEEE
14 years 11 months ago
Generative Graphical Models for Maneuvering Object Tracking and Dynamics Analysis
We study the challenging problem of maneuvering object tracking with unknown dynamics, i.e., forces or torque. We investigate the underlying causes of object kinematics, and propo...
Xin Fan, Guoliang Fan
ICCV
2011
IEEE
12 years 9 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille