What are neural network-based approaches for modeling sequential data that relate to Markov processes?

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 ... 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. transition matrix. Page 3. 151. 8.2 Definitions. The Markov chain is the process X0, ... A Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability of each event depends only on the ... Sep 12, 2011 ... 5 Answers 5 · In martingale, the expectation of the next value IS the present value, so this property is sometimes called 'fair game'. · In ... Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each ... 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. An increasingly popular method for fitting complex models, particularly with a hierchical structure involvese the use of Markov Chain Monte Carlo simulation ... Feb 21, 2012 ... This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two ... ... Markov process. Electronic Journal of Probability, 29 . pp. 1-26. doi ... Branching processes, Markov processes, Decomposition (Mathematics), Stochastic processes ...