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10.009  Articles
1 of 1.002 pages  |  10  records  |  more records»
Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditio... see more

Bayesian networks (BNs) are one of the most widely used class for machine learning and decision making tasks especially in uncertain domains. However, learning BN structure from data is a typical NP-hard problem. In this paper, we present a novel hybrid a... see more

The simulation of complex physics models may lead to enormous computer running times. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner. If for a few input parameter settings an output data... see more

In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used... see more

In this paper, a Bayesian-Network-based model is proposed to optimize the Global Adaptive e-Learning Process (GAeLP). This model determines the type of personalization required for a learner according to his or her real needs, in which we have considered ... see more

Stroke is a condition in which people lose function due to blockage or rupture of blood vessels which results in death. Stroke can affect anyone and anytime, regardless of age. In Indonesia for every 1,000 people, 8 are affected by stroke. Patients with s... see more

Hyperparameter selection has always been the key to machine learning. The Bayesian optimization algorithm has recently achieved great success, but it has certain constraints and limitations in selecting hyperparameters. In response to these constraints an... see more

The Bayesian approach in regression models has shown good results in parameter estimations,where it can increase accuracy and precision. The objective of the current study was to analyze the applicationof Bayesian statistics to the modeling yield for leaf... see more

This paper aims to find a suitable decision rule for a binary composite hypothesis-testing problem with a partial or coarse prior distribution. To alleviate the negative impact of the information uncertainty, a constraint is considered that the maximum co... see more

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