Results 61 to 70 of about 3,284,626 (292)

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Bayesian network–response regression [PDF]

Bioinformatics, 2017
Abstract Motivation There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited.
Lu Wang 0015, Daniele Durante, Rex E. Jung, David B. Dunson   +3 more
openaire   +4 more sources

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Integrating Boolean Satisfiability Algorithms into Bayesian Networks for Accelerated Deterministic Inference

Algorithms
Exact probabilistic inference in Bayesian Networks (BNs) becomes increasingly expensive as network size and structural complexity grow, limiting its applicability in time-sensitive decision-support systems.
Efraín Evaristo Díaz Macías, José Steven Cordero Bazurto, Byron Wladimir Oviedo Bayas   +2 more
doaj   +1 more source

Analyzing Uncertainty in Complex Socio-Ecological Networks

Entropy, 2020
Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain.
Ana D. Maldonado, María Morales, Pedro A. Aguilera, Antonio Salmerón   +3 more
doaj   +1 more source

Achieving career satisfaction through fostering innovation: lessons from the engineering profession in the Australian public sector

Engineering Management in Production and Services, 2021
This paper proposes a novel approach that integrates the capability of empirical validation of structural equation modelling (SEM) and the prediction ability of Bayesian networks (BN).
Wipulanusat Warit, Panuwatwanich Kriengsak, Stewart Rodney A., Sunkpho Jirapon, Thamsatitdej Poomporn   +4 more
doaj   +1 more source

Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System

Journal of Transportation and Logistics, 2023
The traffic problem in Intelligent Transportation Systems has recently become a very important issue. Thanks to Intelligent Transportation Systems, the formation of large amounts of traffic data has led to the formation of data-oriented models.
Cihan Çiftçi, Halim Kazan
doaj   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source

Minimax Bayesian Neural Networks

Entropy
Robustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field.
Junping Hong, Ercan Engin Kuruoglu
doaj   +1 more source

The variance of causal effect estimators for binary v-structures

Journal of Causal Inference, 2022
Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
doaj   +1 more source