Results 41 to 50 of about 1,871,607 (274)

Bayesian evaluation of informative hypotheses in SEM using M plus : A black bear story

European Journal of Developmental Psychology, 2013
Half in jest we use a story about a black bear to illustrate that there are somediscrepancies between the formal use of the p-value and the way it is often usedin practice. We argue that more can be learned from data by evaluatinginformative hypotheses, than by testing the traditional null hypothesis.
Van de Schoot, Rens, Verhoeven, Marjolein, Hoijtink, Herbert   +2 more
openaire   +6 more sources

Evaluating Machine Learning Approaches in Structural Equation Modelling to Improve Predictive Accuracy in Marketing Research

Indonesian Journal of Business and Entrepreneurship
Background: This study aimed to fill a critical research gap by comparing traditional Structural Equation Modelling (SEM) with hybrid Bayesian-Machine Learning (ML) models in marketing research, focusing on the limited exploration of these advanced ...
Chacha Magasi
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

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

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

Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior.

PLoS ONE
Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained.
Nur Anisah Mohamed, Ayed R A Alanzi, Azlinna Noor Azizan, Suzana Ariff Azizan, Nadia Samsudin, Hashem Salarzadeh Jenatabadi   +5 more
doaj   +1 more source

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

Advanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio, Jonas Elsborg, Elif Coşkun, Andreas Burger, Pradipkumar Manubhai Leuaa, Thu Pham Anh Phan, Jin Hyun Chang, Christodoulos Chatzichristodoulou   +7 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Bayesian Versus Frequentist Inference in Structural Equation Modeling: Finite-Sample Properties and Economic Applications

Mathematics
Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and ...
Bojan Baškot, Andrej Ševa, Vesna Lešević, Bogdan Ubiparipović   +3 more
doaj   +1 more source

bayesian: Bindings for Bayesian TidyModels

, 2022
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) ,
Bürkner, Paul-Christian, Badr, Hamada S.   +1 more
core   +1 more source