We propose a method for identifying models with good predictive performance in the family of Bayesian log-linear mixed Dirichlet process random effects count data. Their wide applicability makes assessment model crucial many fields, including disclosure risk estimation, which is focus present work. Rather than assessing on whole contingency table, we target specific objective analysis and two-s...

The Bayesian approach to analyzing semi-parametric models are gaining popularity in practice. For the Cox proportional hazard model, it has been shown recently that the posterior is consistent and leads to asymptotically accurate confidence intervals under a Lévy process prior on the cumulative hazard rate. The explicit expression of the posterior distribution together with independent incremen...

This paper models zero response data from willingness to pay surveys by employing parametric and semi-parametric estimation methods. The result of the specification test indicates the semi-parametric estimation outperforms the parametric estimation significantly.  2001 Published by Elsevier Science B.V.

We analyze reduced basis acceleration of recently proposed deterministic Bayesian inversion algorithms for partial differential equations with uncertain distributed parameter, for observation data subject to additive, Gaussian observation noise. Specifically, Bayesian inversion of affine-parametric, linear operator families on possibly high-dimensional parameter spaces. We consider “high-fideli...

Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multidimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression fun...

Hierarchical False Discovery Rates: Large-scale Inference for Plate-based High-throughput Phenotyping Methods Hannes Bretschneider 2011 This thesis introduces Hierarchical false discovery rates, a new semi-parametric Bayesian method for the detection of causal links between a genotype and phenotype in high-throughput phenotypic studies. Hierarchical false discovery rates are designed for plate-...

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. ...