Abstract
Markov chains in time, such as simple random walks, are at the heart of probability. In space, due to the absence of an obvious definition of past and future, a range of definitions of Markovianity have been proposed. In this paper, after a brief review, we introduce a new concept of Markovianity that aims to combine spatial and temporal conditional independence.
Information
Published: 1 January 2006
First available in Project Euclid: 28 November 2007
zbMATH: 1131.60044
MathSciNet: MR2306197
Digital Object Identifier: 10.1214/074921706000000185
Subjects:
Secondary:
62M30
Keywords:
Hammersley-Clifford factorisation, marked point process, Markov chain, Monte Carlo sampling, neighbour relation, pairwise interaction, Random sequential adsorption, sequential spatial process
Rights: Copyright © 2006, Institute of Mathematical Statistics
