This paper investigates the use of reinforcement learning in electric power system emergency control. The approach consists of using numerical simulations together with on-policy M...
We present a methodology for learning spline-based probabilistic models for sets of contours, proposing a new Monte Carlo variant of the EM algorithm to estimate the parameters of...
We describe a Monte Carlo method for the numerical computation of the principal eigenvalue of the Laplace operator in a bounded domain with Dirichlet conditions. It is based on th...
Fractional generalizations of the Poisson process and branching Furry process are considered. The link between characteristics of the processes, fractional differential equations ...
Simulated tempering (ST) is an established Markov Chain Monte Carlo (MCMC) methodology for sampling from a multimodal density π(θ). The technique involves introducing an auxilia...