MCMC

Fitting large-scale structured additive regression models

Fitting regression models can be challenging when regression coefficients are high-dimensional. Especially when large spatial or temporal effects need to be taken into account the limits of computational capacities of normal working stations are …

Bayesianische Raum-Zeit-Modellierung in der Epidemiologie

This thesis is concerned with the analysis of spatial and temporal structures of epidemiological data using modern Bayes techniques. Mainly autoregressive distributions as Gaussian Markov random fields or random walks are used as smoothing priors. Such extensive models can be estimated using MCMC methods only. Some effective algorithms are introduced to get estimates in acceptable time. Especially for space time interactions such algorithms are essential. As example spatial Bayesian models are applied for wildlife disease incidence data.