What are the applications of Markov processes in cognitive science?

a simple stochastic process in which the distribution of future states depends only on the present state and not on how it arrived in the present state. Dec 5, 2022 ... A continuous-time Markov process X can be conditioned to be in a given state at a fixed time T>0 using Doob's h-transform. Feb 21, 2012 ... This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two ... In this paper we describe a general-purpose model of medical prognosis based on the Markov process and show how this simple mathematical tool may be used to ... Dec 1, 2023 ... We develop a unified software reliability modeling framework based on the NHMPs and apply it to the software reliability prediction. The time-homogeneous Markov model asserts that one protein sequence is derived from another by a series of independent mutations, each changing one character in ... Jan 20, 2021 ... We derive an empirical value function that forms our points gained performance assessment metric, which quantifies the value created or destroyed on any play. Apr 23, 2022 ... With the strong Markov and homogeneous properties, the process {Xτ+t:t∈T} given Xτ=x is equivalent in distribution to the process {Xt:t∈T} given ... Markov Chain Monte Carlo (MCMC) simulations allow for parameter estimation such as means, variances, expected values, and exploration of the posterior ... Feb 13, 2020 ... The current mathematical research in non-commutative probability and stochastic processes has been greatly influenced by scientific developments ...