In this work we look at how Fitting’s embedding of first-order classical logic into first-order S4 can help in reasoning when we are interested in satisfaction “in most cases”, when first-order properties are allowed to fail in cases that are considered insignificant. We extend classical semantics by combining a Kripke-style model construction of “significant” events as possible worlds with the...

PURPOSE To investigate the correlation between perfusion-related parameters obtained with intravoxel incoherent motion (IVIM) and classical perfusion parameters obtained with dynamic contrast-enhanced (DCE) magnetic resonance imaging in patients with head and neck squamous cell carcinoma (HNSCC), and to compare direct and asymptotic fitting, the pixel-by-pixel approach, and a region of interest...

Substantial variation, excess zeros, skew and extreme outliers make fitting and predicting health care expenditures rather difficult. This paper presents a Bayesian model that uses the first year of the fourteenth panel (2009-2010) of the nationally representative Medical Expenditures Panel Survey (MEPS) to predict health care expenditures for individuals in the second year. The merits of a Bay...

Previous work by the authors [1] demonstrated that logistic regression can be a fast and accurate data mining tool for life sciences datasets, competitive with modern tools like support vector machines and balltree based K-NN. This paper has two objectives. The first objective is a serious empirical comparison of logistic regression to several classical and modern learners on a variety of learn...

Bayesian information geometry provides a general error decomposition theorem for arbitrary statistical models and a family of information deviations that include Kullback-Leibler information as a special case. When applied to Gaussian measures it takes the classical Hilbert space (Sobolev space) theories for estimation (regression, filtering, approximation, smoothing) as a special case. When th...

In this paper, we develop the idea in [16] to obtain finer results on the structure of Selmer modules for p-adic representations than the usual main conjecture in Iwasawa theory. We determine the higher Fitting ideals of the Selmer modules under several assumptions. Especially, we describe the structure of the classical Selmer group of an elliptic curve over Q, using the ideals defined from mod...

The classical approach to the modeling of discrete time competing risks consists of fitting multinomial logit models where parameters are estimated using maximum likelihood theory. Since the effects of covariates are specific to the target events, the resulting models contain a large number of parameters, even if there are only few predictor variables. Due to the large number of parameters clas...

In this paper, we address the problem of robustly recovering several instances of a curve model from a single noisy data set with outliers. Using M-estimators revisited in a Lagrangian formalism, we derive an algorithm that we call Simultaneous Multiple Robust Fitting (SMRF), which extends the classical Iterative Reweighted Least Squares algorithm (IRLS). Compared to the IRLS, it features an ex...

Hemoglobin is a classical model allosteric protein. Research on hemoglobin parallels the development of key cooperativity and allostery concepts, such as the 'all-or-none' Hill formalism, the stepwise Adair binding formulation and the concerted Monod-Wymann-Changuex (MWC) allosteric model. While it is clear that the MWC model adequately describes the cooperative binding of oxygen to hemoglobin,...