Other models of student distribution

Author(s):  
A.G. Watts
Keyword(s):  
2020 ◽  
Vol 33 (12) ◽  
pp. 3865-3869
Author(s):  
Mourad Dafri ◽  
Abdelaziz Lajimi ◽  
Sofiane Mendaci ◽  
Abdesselam Babouri

Author(s):  
M. Pinelli ◽  
M. Venturini ◽  
M. Burgio

All measurements, although taken as accurately as possible, are subjected to uncertainty. So the analysis of errors and uncertainty is crucial in all applications since such errors need to be estimated and, when possible, reduced. In particular, when gas turbine mathematical models based on the processing of field measurements (such as the Gas Path Analysis models) are used, the evaluation of measurement reliability is a key point. In fact, it has been demonstrated that these kinds of techniques are sensitive to measurement errors: thus, tools for field data processing to evaluate the presence of the so-called outliers are advisable. In this paper, some statistical methodologies for the assessment of the reliability of the measurements taken on a gas turbine are presented. The methodologies, taken from literature and used for historical measurements, are discussed. Moreover, a new methodology, based on a modified t-Student distribution, is proposed.


Author(s):  
Gerardo Mario Ortigoza Capetillo ◽  
Alberto Pedro Lorandi Medina

En este trabajo analizamos escenarios hipotéticos para contagios de COVID-19 durante la elección 2021 en México. Del 2 de abril al 2 de junio 2021 se llevarán a cabo elecciones de diputados federales, diputados locales, gubernaturas y presidencias municipales en lo que es considerada como la elección más grande en la historia de México; se estima que las actividades de las campañas electorales y el día de la votación se incrementará la movilidad de las personas y con ello su riesgo de contagio por COVID-19. Usando datos históricos de razones de contagios se define la media de estos datos, su desviación estándar y mediante una distribución t-Student se obtiene un intervalo de 90% de confianza para la media. Se utilizan el centro y ambos extremos de este intervalo como tasas de incremento para simular el crecimiento de casos en dos periodos (primer mes: elección diputados federales; segundo mes: elección gubernaturas, diputados locales y ayuntamientos); se reportan simulaciones usando algoritmos de aprendizaje de máquina a 2 meses pasadas las elecciones.Palabras clave: aprendizaje máquina, proyecciones COVID-19, elección 2021 México.SUMMARYIn this work we analyze hypothetical scenarios for COVID-19 infections during the 2021 election in Mexico; from april 2 to june 2, 2021, elections for federal deputies, local deputies, governorships and municipal presidencies will be held in what is considered the largest election in Mexico´s history; it is estimated that the activities of the electoral campaigns and the election day will increase the mobility of people and with it their risk of contagion by COVID-19. Using historical data on infection rates, the mean of these data is defined, its standard deviation and a t-Student distribution is used to obtain a 90% confidence interval for the mean. The center and both ends of this interval are used as rates of increase to simulate the growth of cases in two periods (first month; election of federal deputies; second month; election of governorships, local deputies and municipalities), simulations are reported using machine learning algorithms 2 monts after the elections.Keywords: machine learning, COVID-19 projections, Mexico 2021 electionINTRODUCCIÓNAl momento de escribir este trabajo, se han confirmado alrededor de 110 millones de casos de


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