Nonparametric Estimation of the Probability of Detection of Flaws in an Industrial Component, from Destructive and Nondestructive Testing Data, Using Approximate Bayesian Computation.

We consider the problem of estimating the probability of detection (POD) of flaws in an industrial steel component. Modeled as an increasing function of the flaw height, the POD characterizes the detection process; it is also involved in the estimation of the flaw size distribution, a key input parameter of physical models describing the behavior of the steel component when submitted to extreme thermodynamic loads. Such models are used to assess the resistance of highly reliable systems whose failures are seldom observed in practice.

Exploration of Daphnia behavioral effect profiles induced by a broad range of toxicants with different modes of action.

Behavior is increasingly reported as a sensitive and early indicator of toxicant stress in aquatic organisms. However, the systematic understanding of behavioral effects and comparisons between effect profiles is hampered because the available studies are limited to few chemicals and differ in the exposure conditions and effect parameters examined. The aims of the present study were 1) to explore behavioral responses of Daphnia magna exposed to different toxicants, 2) to compare behavioral effect profiles with regard to chemical modes of action, and 3) to determine the sensitivity and response time of behavioral parameters in a new multi-cell exposure system named Multi-DaphTrack compared with currently utilized tests.

Estimating hazardous concentrations by an informative Bayesian approach.

The species sensitivity distribution (SSD) approach is recommended for assessing chemical risk. In practice, however, it can be used only for the few substances for which large-scale ecotoxicological results are available. Indeed, the statistical frequentist approaches used for building SSDs and for deriving hazardous concentrations (HC5) inherently require extensive data to guarantee goodness-of-fit.

Anatomically informed bayesian model selection for fMRI group data analysis.

A new approach for fMRI group data analysis is introduced to overcome the limitations of standard voxel-based testing methods, such as Statistical Parametric Mapping (SPM). Using a Bayesian model selection framework, the functional network associated with a certain cognitive task is selected according to the posterior probabilities of mean region activations, given a pre-defined anatomical parcellation of the brain. This approach enables us to control a Bayesian risk that balances false positives and false negatives, unlike the SPM-like approach, which only controls false positives.

Mixed-effect statistics for group analysis in fMRI: a nonparametric maximum likelihood approach.

This technical note describes a collection of test statistics accounting for estimation uncertainties at the within-subject level, that can be used as alternatives to the standard t statistic in one-sample random-effect analyses, i.e. when testing the mean effect of a population.

High level group analysis of FMRI data based on Dirichlet process mixture models.

Inferring the position of functionally active regions from a multi-subject fMRI dataset involves the comparison of the individual data and the inference of a common activity model. While voxel-based analyzes, e.g.