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- W64543266 abstract "Abstract A class of spatial mixed models is introduced first. Spatial mixed models include latent Markov random fields, which make their likelihood functions complex. This complexity in turn makes statistical inferences (e.g., parameter estimates and prediction of latent fields) prohibitively difficult. Therefore, two algorithms are also introduce by integrating recent developments in stochastic approximation algorithms and Monte Carlo methods. The first of these algorithms, a stochastic approximation expectation-maximization (SAEM) algorithm, is developed to estimate the strength of spatial regularization in latent Markov random fields and other parameters. The second algorithm, an annealing stochastic approximation Monte Carlo (ASAMC) algorithm, is proposed to compute optimal estimates of latent fields, which are the global maxima of the likelihood functions of complete data. These algorithms are applied to data sets of the distribution of vegetation species and simulated images to demonstrate their effectiveness." @default.
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- W64543266 date "2007-01-01" @default.
- W64543266 modified "2023-09-23" @default.
- W64543266 title "Stochastic Approximation Algorithms for Estimation of Spatial Mixed Models" @default.
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- W64543266 doi "https://doi.org/10.1016/b978-044452044-9/50021-5" @default.
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