Discover how Markov chains predict real systems, from Ulam and von Neumann’s Monte Carlo to PageRank, so you can grasp ...

Markov chains provide a fundamental framework for modelling stochastic processes, where the next state depends solely on the current state. Hidden Markov models (HMMs) extend this framework by ...

Probabilistic model checking and Markov decision processes (MDPs) form two interlinked branches of formal analysis for systems operating under uncertainty. These techniques offer a mathematical ...

A 30-minute talk about Markov modeling generally, with specific reference to the seminal 1986 contribution of Professor Eaves, which described Markov processes for genetic and environmental variance ...

Intelligent systems may encounter environments characterized by varying levels of uncertainty, limited visibility, and continuous shifts. As these systems expand into areas such as autonomous mobility ...