Método de Montecarlo
Método de Montecarlo
Método de Montecarlo
IV. CARACTERÍSTICAS DE CONVERGENCIA DEL MÉTODO Así mismo, el valor de 𝜎 2 (𝐹) también es desconocido
DE MONTECARLO
Por lo que es necesario utilizar el estimador definido
en la ecuación:
El inconveniente radica en que la variable del denominador Nathaly Ivette Mantilla Calderon was
es el estimador de la esperanza de la función de prueba, sobre born in Quito, Ecuador, in 1995. She
el cual no se puede influir directamente. Esto limita a dos las completed his primary studies at Centro
posibilidades de obtención de un coeficiente de variación Educativo Integral Antonio Flores.
Afterwards she completed secondary
reducido.
studies at the Hypatia Cárdenas de
La más directa de estas posibilidades corresponde a Bustamante school where she obtained her
incrementar el número de muestras, sin embargo, es muy bachelor's degree in Mathematical Physics
in 2013. She is
costosa desde el punto de vista de los recursos
currently studying at the Salesiana Polytechnic University in
computacionales y lo que se quiere es obtener resultados the career of Electrical Engineering campus Kennedy.
precisos con el menor número de muestras posibles.
Cristhian Damian Vega
Sí en cualquier punto de la implementación es posible
Guacollantes was born in Quito, Ecuador
reemplazar el valor de una estimación por el valor real del in 1995. He completed his primary studies
parámetro estimado, esto se traduce en una reducción del error at the "Carlos Aguilar" educational center,
final de muestreo. and afterwards he completed secondary
studies at Colegio Técnico Salesiano Don
Así, la reducción de varianza es una forma de hacer mejor Bosco, where he obtained his technical
uso de la información existente conocida. bachelor's degree in Facilities, Equipment
and Electrical Machines in 2013. He is currently studying at
La forma que toma tal información determina el tipo de the Universidad Politécnica Salesiana in the Electrical
técnica de reducción de varianza que puede ser utilizada, Engineering career at Kennedy campus.
mientras mayor sea cantidad de información existente
conocida, más efectiva será la reducción de varianza. Víctor H. Villavicencio was born in Santo
Domingo de los Colorados, Ecuador in
Tres métodos de reducción de varianza ampliamente 1994. He received his bachelor’s degree in
aplicados en evaluación de la confiabilidad: Unidad Educativa Marista “Pío XII” and he
is currently studying toward his B.Sc. in
Variables antitéticas Electrical Engineering at Universidad
Variables de control Politécnica Salesiana (UPS), Quito,
Pichincha, Ecuador.
Muestreo por importancia de estados.
Jorge Luis Jervis Pacheco (1992) was born
in El Carmen, Manabí, Ecuador, on
September 11, 1992. He completed his
primary studies at the "Helena Cortés
Bedoya" School. He graduated from the
Salesian Technical School "Don Bosco", and
is currently studying the 7th level of the
Electrical Engineering Degree at the
Salesian Polytechnic University.
He has worked in various areas, being his last place of work the
company SAYCONT S.A. , in which he worked as an
automation and control technician, as well as an installer.
Among his different distinctions he has obtained: Escort of the
Pavilion of the City of Quito, both in the school and in the
school, several times president of the course, distinctions for the
best student of the school year, as well as multiple prizes in the
contests in which he has participated. Finally, list any awards
and work for IEEE committees and publications.