{"id":"https://openalex.org/W4415138212","doi":"https://doi.org/10.3390/sym17101723","title":"Bayesian Inference on Stress\u2013Strength Reliability with Geometric Distributions","display_name":"Bayesian Inference on Stress\u2013Strength Reliability with Geometric Distributions","publication_year":2025,"publication_date":"2025-10-13","ids":{"openalex":"https://openalex.org/W4415138212","doi":"https://doi.org/10.3390/sym17101723"},"language":"en","primary_location":{"id":"doi:10.3390/sym17101723","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17101723","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/sym17101723","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063865995","display_name":"Mohammed Shakhatreh","orcid":"https://orcid.org/0000-0001-8801-0219"},"institutions":[{"id":"https://openalex.org/I156983542","display_name":"Jordan University of Science and Technology","ror":"https://ror.org/03y8mtb59","country_code":"JO","type":"education","lineage":["https://openalex.org/I156983542"]}],"countries":["JO"],"is_corresponding":true,"raw_author_name":"Mohammed K. Shakhatreh","raw_affiliation_strings":["Department of Mathematics and Statistics, Faculty of Science, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Faculty of Science, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan","institution_ids":["https://openalex.org/I156983542"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5063865995"],"corresponding_institution_ids":["https://openalex.org/I156983542"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29961211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"10","first_page":"1723","last_page":"1723"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10396","display_name":"Fatigue and fracture mechanics","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.8555999994277954},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6904000043869019},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5539000034332275},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5479000210762024},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5472999811172485},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.5055000185966492},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5041999816894531},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/bayes-factor","display_name":"Bayes factor","score":0.4814000129699707}],"concepts":[{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.8555999994277954},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6904000043869019},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5539000034332275},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5472999811172485},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5462999939918518},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.5055000185966492},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5041999816894531},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.48570001125335693},{"id":"https://openalex.org/C142291917","wikidata":"https://www.wikidata.org/wiki/Q4165283","display_name":"Bayes factor","level":4,"score":0.4814000129699707},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45890000462532043},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C149569020","wikidata":"https://www.wikidata.org/wiki/Q25098598","display_name":"Bayesian average","level":5,"score":0.4059000015258789},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39399999380111694},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3743000030517578},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.36500000953674316},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.36160001158714294},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.3610000014305115},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.3522999882698059},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.34709998965263367},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.28619998693466187},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.27390000224113464},{"id":"https://openalex.org/C39947850","wikidata":"https://www.wikidata.org/wiki/Q729523","display_name":"Geometric distribution","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/sym17101723","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17101723","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/sym17101723","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17101723","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W202524383","https://openalex.org/W1999228702","https://openalex.org/W2005587766","https://openalex.org/W2023771657","https://openalex.org/W2025030032","https://openalex.org/W2059881820","https://openalex.org/W2069850126","https://openalex.org/W2086783012","https://openalex.org/W2093223772","https://openalex.org/W2097000183","https://openalex.org/W2111669074","https://openalex.org/W2153793762","https://openalex.org/W2313468107","https://openalex.org/W2314944105","https://openalex.org/W2469424979","https://openalex.org/W2904185096","https://openalex.org/W2941385519","https://openalex.org/W2963029547","https://openalex.org/W3134385088","https://openalex.org/W3154269256","https://openalex.org/W3193814249","https://openalex.org/W4211089296","https://openalex.org/W4232906492","https://openalex.org/W4300000261","https://openalex.org/W4382459344","https://openalex.org/W4388846484"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"the":[3,6,41,53,60,84,91,94],"estimation":[4],"of":[5,59,83,93],"stress\u2013strength":[7],"reliability":[8],"parameter":[9],"\u03c1=P(X\u2264Y),":[10],"where":[11],"stress":[12],"(X)":[13],"and":[14,32,37,79],"strength":[15],"(Y)":[16],"are":[17,26,45],"independently":[18],"modeled":[19],"by":[20,28],"geometric":[21],"distributions.":[22],"Objective":[23],"Bayesian":[24,62],"approaches":[25],"employed":[27],"developing":[29],"Jeffreys,":[30],"reference,":[31],"probability-matching":[33],"priors":[34],"for":[35],"\u03c1,":[36],"their":[38],"effects":[39],"on":[40,72],"resulting":[42],"Bayes":[43],"estimates":[44],"examined.":[46],"Posterior":[47],"inference":[48],"is":[49,64,96],"carried":[50],"out":[51],"using":[52,98],"random-walk":[54],"Metropolis\u2013Hastings":[55],"algorithm.":[56],"The":[57],"performance":[58],"proposed":[61],"estimators":[63],"assessed":[65],"through":[66],"extensive":[67],"Monte":[68],"Carlo":[69],"simulations":[70],"based":[71],"average":[73],"estimates,":[74],"root":[75],"mean":[76],"squared":[77],"errors,":[78],"frequentist":[80],"coverage":[81],"probabilities":[82],"highest":[85],"posterior":[86],"density":[87],"credible":[88],"intervals.":[89],"Furthermore,":[90],"applicability":[92],"methodology":[95],"demonstrated":[97],"two":[99],"real":[100],"data":[101],"sets.":[102]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-14T00:00:00"}
