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ICML
1999
IEEE
14 years 5 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
MICCAI
2007
Springer
14 years 5 months ago
MCMC Curve Sampling for Image Segmentation
Abstract. We present an algorithm to generate samples from probability distributions on the space of curves. We view a traditional curve evolution energy functional as a negative l...
Ayres C. Fan, John W. Fisher III, William M. Wells...
STOC
1997
ACM
125views Algorithms» more  STOC 1997»
13 years 9 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
ICRA
2010
IEEE
126views Robotics» more  ICRA 2010»
13 years 3 months ago
Simulation-based LQR-trees with input and state constraints
— We present an algorithm that probabilistically covers a bounded region of the state space of a nonlinear system with a sparse tree of feedback stabilized trajectories leading t...
Philipp Reist, Russ Tedrake
BMCBI
2007
153views more  BMCBI 2007»
13 years 4 months ago
Estimating genealogies from linked marker data: a Bayesian approach
Background: Answers to several fundamental questions in statistical genetics would ideally require knowledge of the ancestral pedigree and of the gene flow therein. A few examples...
Dario Gasbarra, Matti Pirinen, Mikko J. Sillanp&au...