Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications
McGrory, Clare A., Pettitt, Anthony N., Reeves, Robert, Griffin, Mark, & Dwyer, Mark (2011) Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications. Journal of Computational and Graphical Statistics.
Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.
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|Item Type:||Journal Article|
|Additional Information:||Taylor & Francis FirstOnline Publication|
|Keywords:||Variational Bayes, Discrete Markov Random Field Modeling, Reduced Dependence Approximation, Bayesian analysis, Image analysis, Spacial Data|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)|
|Divisions:||Current > QUT Faculties and Divisions > Division of Technology, Information and Learning Support|
Current > Research Centres > High Performance Computing and Research Support
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||22 Feb 2012 08:00|
|Last Modified:||22 Feb 2012 08:00|
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