Markov chains on a measurable state space information

A Markov chain on a measurable state space is a discrete-time-homogeneous Markov chain with a measurable space as state space. The definition of Markov...

see Markov chains on a measurable state space. A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain...

space can be generalized to chains with uncountable state space through Harris chains. The use of Markov chains in Markov chain Monte Carlo methods covers...

the present state; that is, given the present, the future does not depend on the past. A process with this property is said to be Markov or Markovian...

in distribution. The Markov chain central limit theorem can be guaranteed for functionals of general state space Markov chains under certain conditions...

was awarded a Doctor of Science degree from the ANU for his major contributions to the theory of Markov chains on a measurable state space. Tweedie joined...

scientists. Markov processes and Markov chains are named after Andrey Markov who studied Markov chains in the early 20th century. Markov was interested...

construction of the measurable structure of the action is more complicated. Let S {\displaystyle S} be a finite set. A Markov chain on S {\displaystyle S}...

processes, a Harris chain is a Markov chain where the chain returns to a particular part of the state space an unbounded number of times. Harris chains are regenerative...

probability spaces, otherwise it becomes obscure. A random variable X is a measurable function X: Ω → S from the sample space Ω to another measurable space S called...

necessary. Under this definition only measurable subsets of the sample space, constituting a σ-algebra over the sample space itself, are considered events. An...

system; some elementary contradistinctions between Markov chain and random dynamical system descriptions of a stochastic dynamics are discussed. Let f : R d...

A , A , P ) {\displaystyle (A,{\mathcal {A}},P)} is a probability space and that ( B , B ) {\displaystyle (B,{\mathcal {B}})} is a measurable space whose...

process, a Markov chain taking values in a given state space with a given transition matrix, infinite products of (inner-regular) probability spaces. According...

Markov additive process Markov blanket / Bay Markov chain mixing time / (L:D) Markov decision process Markov information source Markov kernel Markov logic...

geodesic flow on Riemannian manifolds, starting with the results of Eberhard Hopf for Riemann surfaces of negative curvature. Markov chains form a common context...

as chains). Every chain follows a random, three dimensional path through the polymer liquid and is in contact with thousands of other nearby chains. When...

recapture Markov additive process Markov blanket Markov chain Markov chain geostatistics Markov chain mixing time Markov chain Monte Carlo Markov decision...

process, X, is not a Markov process, and it is called an Itô process and not a diffusion process. When the coefficients depends only on present and past...

Roychowdhury, Mrinal Kanti; Rudolph, Daniel J. (2009). "Any two irreducible Markov chains are finitarily orbit equivalent". Israel Journal of Mathematics. 174...

a measurable space ( X , A ) {\displaystyle ({\mathcal {X}},{\mathcal {A}})} . Given that probabilities of events of the form { ω ∈ Ω ∣ X ( ω ) ∈ A }...

unscented Kalman filter which work on nonlinear systems. The basis is a hidden Markov model such that the state space of the latent variables is continuous...

changes of state are occurring at a measurable rate." There are two reservations stated here; the system is isolated; any changes of state are immeasurably...