The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families, and are particularly simple starting from Jeffreys invariant prior. Because of the i.i.d. nature of bootstrap sampling, familiar formulas describe the computational accuracy of the Bayes estimates...

Predicting the future location of users in wireless networks has numerous important applications, and could in turn help service providers to improve the quality of service perceived by their clients (e.g. by applying smart data prefetching and resource allocation strategies). Many location predictors have been devised over the last decade. The predictors proposed so far estimate the next locat...

Estimating the risk, P(X > Y), in probabilistic environmental risk assessment of nanoparticles is a problem when confronted by potentially small risks and small sample sizes of the exposure concentration X and/or the effect concentration Y. This is illustrated in the motivating case study of aquatic risk assessment of nano-Ag. A non-parametric estimator based on data alone is not sufficient as ...

Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric models when evaluating likelihoods is difficult. Central to the success of ABC methods, which have been used frequently in biology, is computationally inexpensive simulation of data sets from the parametric model of interest. However, when simulating data sets fr...

We address a basic question in Bayesian analysis: Can updates of the posterior under observations be represented as a closed-form mapping from the data to the posterior parameters? The question is closely related to the concept of a conjugate prior, but we do not assume that prior and posterior belong to the same model class. We refer to models for which a closed-form mapping exists as function...