This report gives an overview of the sixth Workshop on Formal Techniques for Java-like Programs a... more This report gives an overview of the sixth Workshop on Formal Techniques for Java-like Programs at ECOOP 2004. It explains the motivation for the a workshop and summarises the presentations and discussions. The title of this report should be referenced as "Report from the ECOOP 2004 Workshop on Formal Techniques for Java-like Programs (FTfJP)".
ion techniques are indispensable for the specification and verification of the functional behavio... more ion techniques are indispensable for the specification and verification of the functional behaviour of programs. In object-oriented specification languages like Java Modeling Language, a powerful abstraction technique is the use of model classes, that is, classes that are only used for specification purposes and that provide object-oriented interfaces for essential mathematical concepts such as sets or relations. Although the use of model classes in specifications is natural and powerful, they pose problems for verification. Program verifiers map model classes to their underlying logics. Flaws in a model class or the mapping can easily lead to unsoundness and incompleteness. This article proposes an approach for the faithful mapping of model classes to mathematical structures provided by the theorem prover of the program verifier at hand. Faithfulness means that a given model class semantically corresponds to the mathematical structure it is mapped to. This approach enables reasoning about programs specified in terms of model classes. It also helps in writing consistent and complete model-class specifications as well as in identifying and checking redundant specifications.
We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMM... more We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of a cubic smoothing spline, and the random effect distribution using a normal distribution and alternatively a nonparametric Dirichlet process (DP) prior. When the random effect distribution is assumed to be normal, we propose a uniform shrinkage prior (USP) for the variance components and the smoothing parameter. When the random effect distribution is modeled nonparametrically, we use a DP prior with a normal base measure and propose a USP for the hyperparameters of the DP base measure. We argue that the commonly assumed DP prior implies a non-zero mean of the random effect distribution, even when a base measure with mean zero is specified. This implies weak identifiability for the fixed effects, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function. We propose an adjustment using a post-processing technique. We show that under mild conditions the posterior is proper under the proposed USP, a flat prior for the fixed effect parameters, and an improper prior for the residual variance. We illustrate the proposed approach using a longitudinal hormone dataset, and carry out extensive simulation studies to compare its finite sample performance with existing methods.
We discuss inference for a human phage display experiment with three stages. The data are tripept... more We discuss inference for a human phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. We formalize the research question as inference about the monotonicity of mean counts over stages. The inference goal is then to identify a list of peptidetissue pairs with significant increase over stages. We use a semi-parametric Dirichlet process mixture Biometrics XX, 1-?? DOI: XX.XXXX/j.XXXX-XXXX.20XX.XXXXX.x Month 200X of Poisson model. The posterior distribution under this model allows the desired inference about the monotonicity of mean counts. However, the desired inference summary as a list of peptide-tissue pairs with significant increase involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.
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Papers by Peter Müller