References:
1. Poroshin A.A., Bobrinev E.V., Udavtsova E.Yu., Kondashov A.A. Dynamic Model for Assessing the State of the Occupational Health and Safety Management System. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2021. № 6. pp. 28–33. (In Russ.). DOI: 10.24000/0409-2961-2021-6-28-33
2. Downey A.B. Think Bayes: Bayesian Statistics Made Simple. Needham: Green Tea Press, 2012. 210 p.
3. Zhukovskiy E.V., Kalinin M.O., Marshev I.I. Detection of malicious executable files based on machine learning algorithms. Problemy informatsionnoy bezopasnosti. Kompyuternye sistemy = Information Security Problems. Computer Systems. 2019. № 1. pp. 89–99. (In Russ.).
4. Zavyalov Ya.O. Investment strategies based on anomalies. Put nauki = The Way of Science. 2015. № 10 (20). pp. 56–60. (In Russ.).
5. Lykova O.V. Automatic Detection of the Main Markers of Insincerity in Texts. Vestnik Moskovskogo gosudarstvennogo lingvisticheskogo universiteta. Gumanitarnye nauki = Vestnik of Moscow State Linguistic University = Humanitarian Science. 2019. Iss. 7 (823). pp. 146–154. (In Russ.).
6. Kharya S., Agrawal S., Soni S. Naive Bayes classifiers: A probabilistic detection model for breast cancer. International Journal of Computer Applications. 2014. Vol. 92. Iss. 10. pp 26–31. DOI: 10.5120/16045-5206
7. Mandal S.K. Performance analysis of data mining algorithms for breast cancer cell detection using Naive Bayes, logistic regression and decision Tree. International Journal of Engineering and Computer Science. 2017. Vol. 6. Iss. 2. pp. 20388–20391. DOI: 10.18535/ijecs/v6i2.40
8. Jing Y., Pavlovic V., Rehg J.M. Boosted Bayesian network classifiers. Machine Learning. 2008. Vol. 73. Iss. 2. pp. 155–184. DOI: 10.1007/s10994-008-5065-7
9. Bassett R., Deride J. Maximum a posteriori estimators as a limit of Bayes estimators. Mathematical Programming. 2019. Vol. 174. Iss. 1–2. pp. 129–144. DOI: 10.1007/s10107-018-1241-0
10. Lyubushin N.P., Brikach G.E. Harrington's generalized desirability function in the multiparameter economic problems. Ekonomicheskiy analiz: teoriya i praktika = Economic analysis: theory and practice. 2014. № 18 (370). pp. 2–10. (In Russ.).
11. Poroshin A.A., Udavtsova E.Yu., Bobrinev E.V., Kondashov A.A., Kharin V.V. Assessment of Fire Hazard Level of Industrial Objects based on the Statistic Methods. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2020. № 3. pp. 12–17. (In Russ.). DOI: 10.24000/0409-2961-2020-3-12-17
12. Poroshin A.A., Shishkov M.V., Mashtakov V.A., Putin V.S., Bobrinev E.V. Dependence of the Traumatism of Firemen on Complexity of the Fire. Pozharnaya bezopasnost = Fire safety. 2013. № 2. pp. 92–94. (In Russ.).
2. Downey A.B. Think Bayes: Bayesian Statistics Made Simple. Needham: Green Tea Press, 2012. 210 p.
3. Zhukovskiy E.V., Kalinin M.O., Marshev I.I. Detection of malicious executable files based on machine learning algorithms. Problemy informatsionnoy bezopasnosti. Kompyuternye sistemy = Information Security Problems. Computer Systems. 2019. № 1. pp. 89–99. (In Russ.).
4. Zavyalov Ya.O. Investment strategies based on anomalies. Put nauki = The Way of Science. 2015. № 10 (20). pp. 56–60. (In Russ.).
5. Lykova O.V. Automatic Detection of the Main Markers of Insincerity in Texts. Vestnik Moskovskogo gosudarstvennogo lingvisticheskogo universiteta. Gumanitarnye nauki = Vestnik of Moscow State Linguistic University = Humanitarian Science. 2019. Iss. 7 (823). pp. 146–154. (In Russ.).
6. Kharya S., Agrawal S., Soni S. Naive Bayes classifiers: A probabilistic detection model for breast cancer. International Journal of Computer Applications. 2014. Vol. 92. Iss. 10. pp 26–31. DOI: 10.5120/16045-5206
7. Mandal S.K. Performance analysis of data mining algorithms for breast cancer cell detection using Naive Bayes, logistic regression and decision Tree. International Journal of Engineering and Computer Science. 2017. Vol. 6. Iss. 2. pp. 20388–20391. DOI: 10.18535/ijecs/v6i2.40
8. Jing Y., Pavlovic V., Rehg J.M. Boosted Bayesian network classifiers. Machine Learning. 2008. Vol. 73. Iss. 2. pp. 155–184. DOI: 10.1007/s10994-008-5065-7
9. Bassett R., Deride J. Maximum a posteriori estimators as a limit of Bayes estimators. Mathematical Programming. 2019. Vol. 174. Iss. 1–2. pp. 129–144. DOI: 10.1007/s10107-018-1241-0
10. Lyubushin N.P., Brikach G.E. Harrington's generalized desirability function in the multiparameter economic problems. Ekonomicheskiy analiz: teoriya i praktika = Economic analysis: theory and practice. 2014. № 18 (370). pp. 2–10. (In Russ.).
11. Poroshin A.A., Udavtsova E.Yu., Bobrinev E.V., Kondashov A.A., Kharin V.V. Assessment of Fire Hazard Level of Industrial Objects based on the Statistic Methods. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2020. № 3. pp. 12–17. (In Russ.). DOI: 10.24000/0409-2961-2020-3-12-17
12. Poroshin A.A., Shishkov M.V., Mashtakov V.A., Putin V.S., Bobrinev E.V. Dependence of the Traumatism of Firemen on Complexity of the Fire. Pozharnaya bezopasnost = Fire safety. 2013. № 2. pp. 92–94. (In Russ.).