Search Results for author:
Found 3 papers, 0 papers with code
Bayesian Learning for Dynamic Inference
The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity.
Online Markov decision processes with Kullback-Leibler control cost
This paper considers an online (real-time) control problem that involves an agent performing a discrete-time random walk over a finite state space.
Relax but stay in control: from value to algorithms for online Markov decision processes
Online learning algorithms are designed to perform in non-stationary environments, but generally there is no notion of a dynamic state to model constraints on current and future actions as a function of past actions.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
