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Most regression problems in practice require exible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random eeects accounting for overdis-persion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a uniied approach for Bayesian(More)
Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM's and extensions to generalized structured additive regression based on one or two dimensional P-splines as the main building block. The approach extends previous work(More)
To identify epigenetic patterns, which may predispose to type 2 diabetes (T2D) due to a family history (FH) of the disease, we analyzed DNA methylation genome-wide in skeletal muscle from individuals with (FH(+)) or without (FH(-)) an FH of T2D. We found differential DNA methylation of genes in biological pathways including mitogen-activated protein kinase(More)
SUMMARY We present a uniied semiparametric Bayesian approach based on Markov random eld priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial eeects as well as nonlinear(More)
Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors. Using 48 genes located near T2D risk variants, we identified gene coexpression and protein-protein interaction networks(More)
We propose extensions of penalized spline generalized additive models for analyzing space-time regression data and study them from a Bayesian perspective. Non-linear effects of continuous covariates and time trends are modelled through Bayesian versions of penalized splines, while correlated spatial effects follow a Markov random field prior. This allows to(More)
SUMMARY There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow the estimation of very complex and realistic models. This(More)
Converging evidence suggests that the endogenous cannabinoid (eCB) system is involved in extinction of learned behaviours. Using operant and classical conditioning procedures, the potential of the fatty acid amide (FAAH) inhibitor, URB-597, and the CB(1) antagonist/inverse agonist, SR141716, to promote and inhibit (respectively) extinction of learned(More)