We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a co...
Christian R. Shelton, Yu Fan, William Lam, Joon Le...
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
In this paper we develop boundary value methods for detecting Sacker-Sell spectra in discrete time dynamical systems. The algorithms are advancements of earlier methods for comput...
This paper deals with error estimates for space-time discretizations in the context of nary variational inequalities of rate-independent type. After introducing a general abstract ...
Alexander Mielke, Laetitia Paoli, Adrien Petrov, U...
Shortest Remaining Processing Time first (SRPT) has long been known to optimize the queue length distribution and the mean response time (a.k.a. flow time, sojourn time). As such,...