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 ...

Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...

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: This research introduces a novel approach to automated parameter optimization for dynamic impact testing sequences using a combination of Bayesian Optimization (BO) and Finite Element ...

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 ...

Abstract: Radioisotope Thermoelectric Generators (RTGs) are critical power sources for deep-space missions, requiring robust and long-duration performance. Predicting and mitigating heat source ...