normal 0 false false false en-us x-none fa microsoftinternetexplorer4 aim : for the purpose of cost modeling, the semi-parametric single-index two-part model was utilized in the paper. furthermore, as functional gastrointestinal diseases which are well-known as common causes of illness among the society people in terms of both the number of patients and prevalence in a specific time interval, t...

In this paper, we merge the parametric structure of neural networks into a segmental semi-Markov model to set up a Bayesian framework for protein structure prediction. The parametric model, which can also be regarded as an extension of a sigmoid belief network, captures the underlying dependency in residue sequences. The results of numerical experiments indicate the usefulness of this approach.

PURPOSE To evaluate a semi-parametric, model-based approach for obtaining transcription rates from mRNA and protein expression. METHODS The transcription profile input was modeled using an exponential function of a cubic spline and the dynamics of translation; mRNA and protein degradation were modeled using the Hargrove-Schmidt model. The transcription rate profile and the translation, and mR...

We address the problem of automatic analysis of parametric counter and clock automata. We propose a semi-algorithmic approach based on using (1) expressive symbolic representation structures called Parametric DBM's, and (2) accurate extrapolation techniques allowing to speed up the reachability analysis and help its termination. The techniques we propose consist in guessing automatically the ee...

Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features spatial patterns from observed while using only minimal assumptions. However, cannot generate extreme events beyond the range of values. We here propose value theory for stochastic processes extrapolate towards yet unobserved high quantiles. Original first enri...

We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semiparametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations. The graph st...