Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach.
Panos ParpasBerk UstunMort WebsterQuang Kha TranPublished in: INFORMS J. Comput. (2015)
Keyphrases
- stochastic programming
- dynamic programming
- importance sampling
- markov chain monte carlo
- linear program
- markov chain
- parameter estimation
- monte carlo
- generative model
- posterior distribution
- linear programming
- approximate inference
- bayesian inference
- particle filter
- posterior probability
- particle filtering
- stereo matching
- probabilistic model
- graphical models
- bayesian framework
- computer vision
- data association
- belief propagation
- simulated annealing
- visual tracking
- kalman filter
- hyperparameters
- least squares
- optimal solution
- bayesian networks
- image sequences
- machine learning