Abstract: The probability of missed detection (PMD) is a crucial metric for characterizing the integrity performance of receiver autonomous integrity monitoring (RAIM). The time consumption of the ...
MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in a completely unsupervised fashion. Intuitively, MOFA can be viewed as a versatile and ...
A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...
Learn how the probability density function (PDF) helps financial analysts assess the distribution of stock or ETF returns, ...
Abstract: In this paper, a method of generating true random numbers obeying multiple distribution characteristics is proposed. First, two resistance-capacitance (RC) self-excited oscillation circuits ...
The interventions can safely and effectively reduce antibiotic use among patients with ARIs in village clinics in rural China. Future implementation efforts should further examine the barriers and ...
What began with a focus on weather forecasting has evolved toward addressing errors in scientific modeling. In the collaborative environment of the Penn State Institute for Computational and Data ...
[Update] Interested in faster and more accurate structure learning? See our new dagrad library for developing and experimenting with newer differentiable (gradient-based) structure learning methods.
Copyright: © 2026 The Author(s). Published by Elsevier Ltd.
Log-linear Poisson models provide a principled framework for modeling such count data through covariate-dependent intensity functions. This study develops a Bayesian formulation of the Poisson ...
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