A Fast and Exact Simulation Algorithm for General Gaussian Markov Random Fields
نویسندگان
چکیده
This paper presents a fast and exact simulation algorithm for a general Gaussian Markov Random Field (GMRF) defined on a lattice, . For a neighborhood, the algorithm has initializationcost of flops and exact samples are then available using flops. Conditioned samples on general constraints costs only extra flops. The algorithm is easy to generalize to non-rectangular lattices, compute the log-likelihoodof the sample nearly for free and runs very efficient on the computer. An exact and fast simulation algorithm for unconditional and conditional GMRF additional to easy access to the log-likelihood, makes GMRF (even more) computational convenient. GMRF has a potential use when modeling non-stationary spatial fields and is commonly used in hierarchical spatial or spatio-temporal Bayesian models analyzed using Markov chain Monte Carlo.
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