El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Several factors affect the activities of the mining industry. For example, accident rates are critical because they affect company ratings in the stock market (Standard & Poors). Considering that the corporate image is directly related to its stakeholders, this study conducts an accident analysis using quantitative and qualitative methods. In this way, the contingency rate is controlled, mitigated, and prevented while serving the needs) of the stakeholders. The Bayesian network method contributes to decision-making through a set of variables and the dependency relationships between them, establishing an earlier probability of unknown variables. Bayesian models have different applications, such as diagnosis, classification, and decision, and establish relationships among variables and cause–effect links. This study uses Bayesian inference to identify the various patterns that influence operator accident rates at a contractor mining company, and therefore, study and assess the possible differences in its future operations.
| Identifer | oai:union.ndltd.org:PERUUPC/oai:repositorioacademico.upc.edu.pe:10757/656168 |
| Date | 01 January 2020 |
| Creators | Ziegler-Barranco, Ana, Mera-Barco, Luis, Aramburu-Rojas, Vidal, Raymundo, Carlos, Mamani-Macedo, Nestor, Dominguez, Francisco |
| Publisher | Springer |
| Source Sets | Universidad Peruana de Ciencias Aplicadas (UPC) |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/article |
| Format | application/html |
| Source | Universidad Peruana de Ciencias Aplicadas (UPC), Repositorio Académico - UPC, Advances in Intelligent Systems and Computing, 1209 AISC, 350, 358 |
| Rights | info:eu-repo/semantics/embargoedAccess |
| Relation | https://www.springerprofessional.de/en/scat-model-based-on-bayesian-networks-for-lost-time-accident-pre/18134120 |
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