Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
Abstract--We present a methodology for implementing digital signal processing (DSP) operations such as filtering with biomolecular reactions. From a DSP specification, we demonstra...
Hua Jiang, Aleksandra P. Kharam, Marc D. Riedel, K...
We consider impulsive systems with several reset maps triggered by independent renewal processes, i.e., the intervals between jumps associated with a given reset map are identicall...
The analysis of online least squares estimation is at the heart of many stochastic sequential decision-making problems. We employ tools from the self-normalized processes to provi...