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Found 9 papers, 2 papers with code
Approximate Control for Continuous-Time POMDPs
This work proposes a decision-making framework for partially observable systems in continuous time with discrete state and action spaces.
Collaborative Optimization of the Age of Information under Partial Observability
In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver.
Entropic Matching for Expectation Propagation of Markov Jump Processes
This paper addresses the problem of statistical inference for latent continuous-time stochastic processes, which is often intractable, particularly for discrete state space processes described by Markov jump processes.
Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks
The presented blocked Gibbs particle smoothing algorithm utilizes a sequential Monte Carlo method to estimate the latent states and performs distinct Gibbs steps for the parameters of a biochemical reaction network, by exploiting a jump-diffusion approximation model.
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems
Switching dynamical systems are an expressive model class for the analysis of time-series data.
Variational Inference for Continuous-Time Switching Dynamical Systems
Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on an Markov jump process modulating a subordinated diffusion process.
Load Balancing in Compute Clusters with Delayed Feedback
In this paper, we provide a partially observable (PO) model that captures the load balancing decisions in parallel buffered systems under limited information of delayed acknowledgements.
POMDPs in Continuous Time and Discrete Spaces
We consider the problem of optimal decision making in such discrete state and action space systems under partial observability.
Correlation Priors for Reinforcement Learning
Many decision-making problems naturally exhibit pronounced structures inherited from the characteristics of the underlying environment.
