bayesian analysis

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spatial statistics applied statistics bayesian analysis Bayesian and computational Statistics Bayesian Statistics Log-Gaussian Cox process latent Gaussian models Gaussian processes INLA MetricGraph Metric graphs

MetricGraph: A Statistical Framework for Modeling Gaussian Fields on Metric Graphs

Bayesian image analysis in Fourier space (BIFS) models and some relationships with Markov random fields

Prof. John Kornak, Biostatistics, University of California, San Francisco

Oct 14, 16:30 - 17:45

B4 B5 A0215

bayesian analysis

Abstract For over 30 years, Bayesian image analysis has provided an important pathway to image reconstruction and enhancement, by balancing a priori expectations of image characteristics with a model for the noise process. The conventional Bayesian modeling approach defined in image space implements priors that describe inter-dependence between spatial locations (and can therefore be difficult to model and compute). However, similar models can be developed more conveniently in Fourier transformed space as a large set of independent processes. The originally complex high-dimensional estimation