A unified formulation of Gaussian vs. sparse stochastic processes - Part I: Continuous-domain theory
Michael UnserPouya Dehghani TaftiQiyu SunPublished in: CoRR (2011)
Keyphrases
- stochastic processes
- domain theory
- explanation based learning
- stochastic process
- inductive learning
- probability distribution
- domain knowledge
- random fields
- background knowledge
- training examples
- dynamic bayesian networks
- maximum likelihood
- high dimensional
- random variables
- non stationary
- sparse representation
- computer vision
- graphical models
- supervised learning
- active learning
- reinforcement learning