A New Bayesian Approach to Increase Measurement Accuracy Using a Precision Entropy Indicator

A New Bayesian Approach to Increase Measurement Accuracy Using a Precision Entropy Indicator | 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 A New Bayesian Approach to Increase Measurement Accuracy Using a Precision Entropy Indicator Peter Domjan, Viola Angyal, Adam Bertalan, Istvan Vingender, Elek Dinya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5926277/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 Background Our developed Imprecision Entropy Indicator (IEI) represents a novel metric that quantifies the imprecision of data relative to a target based on entropy and statistical concentration. The analysis of data relative to a fixed target is crucial across various fields of healthcare, including radiotherapy, diagnostics, gene expression, and the delineation of surgical target areas. The study hypothesizes that the starting position of the target search influences search time, which can be attributed to the varying levels of uncertainty surrounding the target in addition to its distance. Methods The self-learning search algorithm was developed in Python, where the objective was to locate a target within a circle with a radius of 100 units, starting from a randomly assigned initial point. The search process guided Bayesian optimization based on the measured IEI indicator, minimizing uncertainty in the search steps. The search paths and the number of search steps were evaluated using 1,000 target searches and heatmaps. The algorithm's efficiency was compared with results from random search, random walk, and genetic algorithm methods. A one-way non-parametric ANOVA was used to analyse the effect of the starting point's quadrant placement on the number of search steps. Results The results showed that IEI indicator, machine learning successfully located the target in an average of 8.87 steps. The study examined the informational asymmetry surrounding the target area, which became measurable in the entropy field through the determination of the targeting direction. Due to the directionality, the starting quadrant of the search influenced the number of search steps, which ranged from an average of 7.69 to 9.53. ANOVA analysis (p< 0.001) confirmed that the initial quadrant significantly affects the number of search steps. Conclusion The combined handling of concentration, uncertainty patterns in data, distance, and targeting direction opens a new dimension in optimizing search processes, which, when complemented by an adaptive self-learning system, can become a valuable tool for various healthcare applications. It was demonstrated that the uncertainty of scatter concentration in point diagrams can also be measured based on entropy. Entropy Concentration Measurement Uncertainty Directional Analysis Targeting Full Text Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5926277","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412433767,"identity":"5cbf9c5d-6095-4bf7-9551-80b5fb011057","order_by":0,"name":"Peter 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The analysis of data relative to a fixed target is crucial across various fields of healthcare, including radiotherapy, diagnostics, gene expression, and the delineation of surgical target areas. The study hypothesizes that the starting position of the target search influences search time, which can be attributed to the varying levels of uncertainty surrounding the target in addition to its distance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe self-learning search algorithm was developed in Python, where the objective was to locate a target within a circle with a radius of 100 units, starting from a randomly assigned initial point. The search process guided Bayesian optimization based on the measured IEI indicator, minimizing uncertainty in the search steps. The search paths and the number of search steps were evaluated using 1,000 target searches and heatmaps. The algorithm's efficiency was compared with results from random search, random walk, and genetic algorithm methods. A one-way non-parametric ANOVA was used to analyse the effect of the starting point's quadrant placement on the number of search steps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results showed that IEI indicator, machine learning successfully located the target in an average of 8.87 steps. The study examined the informational asymmetry surrounding the target area, which became measurable in the entropy field through the determination of the targeting direction. Due to the directionality, the starting quadrant of the search influenced the number of search steps, which ranged from an average of 7.69 to 9.53. 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