How can birth-death processes be analyzed?

Feb 10, 2023 ... Rank the spreading influence of nodes using dynamic Markov process. Jianhong Lin, Bo-Lun Chen, Zhao Yang, Jian-Guo Liu and Claudio J. Tessone. Nov 3, 2020 ... Semi-markov processes are those in which the transition between states is given by a state-transition matrix and a dwell (waiting) time in the state. Jun 6, 2023 ... They model query distributions as Markov processes and leverage insights and techniques from stochastic processes and machine learning. We ... Dec 1, 2023 ... We develop a unified software reliability modeling framework based on the NHMPs and apply it to the software reliability prediction. Markov process; spectral method; numerical comparison; tail behavior. Access to Document. 10.1023/A · A_1020909928743Final published version, 71.4 KBLicence ... Title: Markov process models for discrimination learning; Book title: Longitudinal models in the behavioral and related sciences; Pages (from-to): 337-365 ... We introduce and analyze a birth-death-suppression Markov process as a model of controlled culling of an abstract, dynamic population. 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. This transform requires the ... Markov Chain Monte Carlo (MCMC) simulations allow for parameter estimation such as means, variances, expected values, and exploration of the posterior ... Bayesian inference of Markov processes. In: Wiley StatsRef: Statistics Reference Online Book Chapters uri icon. Overview. Overview. book title. Wiley StatsRef ...