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ICML
1999
IEEE
14 years 6 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 6 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 10 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 4 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 5 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...