How are Markov processes used in information theory?

Jul 1, 2003 ... ... process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition ... The MeHiM is based on the idea of the hidden Markov model (HMM; Rabiner 1989; Cappé et al. 2006), an effective tool for modeling stochastic processes. In an HMM ... Dec 23, 2014 ... A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that ... Markov Chain Monte Carlo (MCMC) simulations allow for parameter estimation such as means, variances, expected values, and exploration of the posterior ... 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 ... Nov 14, 2012 ... Finite Math: Markov Chain Steady-State Calculation. In this video, we discuss how to find the steady-state probabilities of a simple Markov ... Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes. Kentaro Katahira, Kenta Suzuki, Kazuo Okanoya, Masato Okada ... An increasingly popular method for fitting complex models, particularly with a hierchical structure involvese the use of Markov Chain Monte Carlo simulation ... Finite Markov Processes. A finite Markov process is a random process on a graph, where from each state you specify the probability of selecting each available ... 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 ...