Test escapes are chips that pass the chip-level test program but fail system-level test or in the field. It is known that statistical analysis based on chip production test data could identify abnormalities for screening test escapes. It has also been shown that from the chip test data, we can generate revealing features for statistical analysis by comparing the measurement data to different references such as the measurement mean of a wafer, the spatial pattern of a wafer, and the measurements of neighboring chips. Given these existing features as the base features, this paper proposes a new class of transformations which could generate additional informative features based on pairwise proximities between chips on the same wafer. Specifically, we apply multiple distance functions in a feature space composed of the base features and calculate the corresponding pairwise proximities between each pair of chips. Each of the resulting proximities could potentially embed some unique information that reveals the abnormalities of some test escapes. Then we convert the proximities into Euclidean vector spaces using constant shift embedding (CSE), which preserves the cluster structure through the conversion, so that traditional outlier analysis algorithms such as local outlier factor (LOF) can be applied. The LOF value and the first dimension in each embedded space are used as additional features for each sample. These new features, jointly analyzed with the base features, provide more revealing information about test escapes and thus further improve the test escape detection rate in our experiment based on production test data.

MOTIVATION: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. RESULTS: We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. AVAILABILITY: Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-dependent variables for the purpose of Bayesian model inference. The model selection criterion maximizes the expected Kullback-Leibler divergence between the prior and the posterior probabilities over the models. The proposed strategy generalizes the standard D-optimal design, which is obtained from a uniform prior with Gaussian noise. In addition, our approach allows us to determine an information halting criterion for model identification. We illustrate the benefits of our approach by differentiating between 18 published biochemical models of the TOR signaling pathway, a model selection problem in systems biology. By generating pivotal selection experiments, our strategy outperforms the standard Aoptimal, D-optimal and E-optimal sequential design techniques.