Bayesian prediction and modeling have emerged as transformative tools in the design and management of clinical trials. By integrating prior knowledge with accumulating trial data, Bayesian methods ...
Abstract: This paper introduces a novel framework, Dynamic Graph Resonance and Bayesian Optimization (DGROBO), for predicting individual differences in functional brain connectivity and their ...
Abstract: Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) ...
In this paper we describe the use of hybrid dynamic Bayesian networks (HDBNs) to model the operational risk faced by financial institutions in terms of economic capital. We describe a methodology for ...
Background: Understanding how different modeling strategies affect associations in nutritional epidemiology is critical, especially given the temporal complexity of dietary and health data. Objective: ...
Abstract: Aiming at the complex dynamic characteristics of nonlinear, large time delay and multivariable strong coupling in the firing process of cement rotary kiln and the insufficient accuracy of ...
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