Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network.
Kyongmin YeoDylan E. C. GrullonFan-Keng SunDuane S. BoningJayant R. KalagnanamPublished in: CoRR (2020)
Keyphrases
- dynamical systems
- model free
- variational inference
- reinforcement learning
- bayesian inference
- state space
- probabilistic graphical models
- function approximation
- topic models
- probabilistic model
- posterior distribution
- closed form
- gaussian process
- graphical models
- latent dirichlet allocation
- bayesian framework
- image segmentation
- mixture model
- approximate inference
- pairwise
- feature extraction