On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms

On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms Volkan Sevinç, Michail Tsagris This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5137132/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Various Bayesian network learning algorithms are proposed in the literature. This article aims to introduce PCTABU as a novel BN learning algorithm and examine and compare the estimation performances of PCTABU and PCHC algorithms, which are built on two different hyperparameters Tabu-Search (TABU) and Hill-Climbing (HC). Moreover, the estimation performances of the two algorithms are compared with respect to three different scoring functions, Bayesian Dirichlet equivalence (BDe), log-likelihood (LL), and Bayes information criterion (BIC) with both simulated and real-data-based Bayesian networks. Artificial Intelligence and Machine Learning Causality Bayesian networks Hill-Climbing Tabu-Search PCTABU PCHC Scoring functions Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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