GenerandoVariablesAleatoria
MarioAlvarez
2025-03-26
#Método de la Tranformada Inversa El método de la transformada inversa se basa en la idea de que cualquier evento aleatorio puede generarse a partir de valores aleatorios uniformes, transformándolos mediante una regla que respeta la forma en que ocurren dichos eventos en la realidad. Si conocemos cómo se distribuyen ciertos sucesos, podemos retroceder en el proceso y convertir números aleatorios en resultados con la distribución deseada. Esta técnica es fundamental en simulación, ya que permite recrear situaciones probabilísticas complejas a partir de una fuente de aleatoriedad básica, similar a convertir una ruleta equilibrada en cualquier otro sistema de eventos aleatorios siguiendo un patrón específico.
- Ejemplo de generar Variables Aleatorias de una exponencial con parametro \(\beta = 3\)
a <- 317
c <- 15
m <- 571
X_n <- 41 # semilla
random.number<-numeric(100) # vector numérico de longitud 50
for (i in 1:100)
{X_n<-(a*X_n+c)%%m
random.number[i]<-X_n/m # números en el intervalo [0,1]
}
random.number## [1] 0.78809107 0.85113835 0.83712785 0.39579685 0.49387040 0.58318739
## [7] 0.89667250 0.27145359 0.07705779 0.45359019 0.81436077 0.17863398
## [13] 0.65323993 0.10332750 0.78108581 0.63047285 0.88616462 0.94045534
## [19] 0.15061296 0.77057793 0.29947461 0.95971979 0.25744308 0.63572680
## [25] 0.55166375 0.90367776 0.49211909 0.02802102 0.90893170 0.15761821
## [31] 0.99124343 0.25043783 0.41506130 0.60070053 0.44833625 0.14886165
## [37] 0.21541156 0.31173380 0.84588441 0.17162872 0.43257443 0.15236427
## [43] 0.32574431 0.28721541 0.07355517 0.34325744 0.83887916 0.95096322
## [49] 0.48161121 0.69702277 0.98248687 0.47460595 0.47635727 0.03152364
## [55] 0.01926445 0.13309982 0.21891419 0.42206655 0.82136602 0.39929947
## [61] 0.60420315 0.55866900 0.12434326 0.44308231 0.48336252 0.25218914
## [67] 0.97022767 0.58844133 0.56217163 0.23467601 0.41856392 0.71103327
## [73] 0.42381786 0.37653240 0.38704028 0.71803853 0.64448336 0.32749562
## [79] 0.84238179 0.06129597 0.45709282 0.92469352 0.15411559 0.88091068
## [85] 0.27495622 0.18739054 0.42907180 0.04203152 0.35026270 0.05954466
## [91] 0.90192644 0.93695271 0.04028021 0.79509632 0.07180385 0.78809107
## [97] 0.85113835 0.83712785 0.39579685 0.49387040# generando una variable exponencial con beta = 3
x <- -3*log(1-random.number)
round(x,1) # redondeando a una cifra decimal## [1] 4.7 5.7 5.4 1.5 2.0 2.6 6.8 1.0 0.2 1.8 5.1 0.6 3.2 0.3 4.6
## [16] 3.0 6.5 8.5 0.5 4.4 1.1 9.6 0.9 3.0 2.4 7.0 2.0 0.1 7.2 0.5
## [31] 14.2 0.9 1.6 2.8 1.8 0.5 0.7 1.1 5.6 0.6 1.7 0.5 1.2 1.0 0.2
## [46] 1.3 5.5 9.0 2.0 3.6 12.1 1.9 1.9 0.1 0.1 0.4 0.7 1.6 5.2 1.5
## [61] 2.8 2.5 0.4 1.8 2.0 0.9 10.5 2.7 2.5 0.8 1.6 3.7 1.7 1.4 1.5
## [76] 3.8 3.1 1.2 5.5 0.2 1.8 7.8 0.5 6.4 1.0 0.6 1.7 0.1 1.3 0.2
## [91] 7.0 8.3 0.1 4.8 0.2 4.7 5.7 5.4 1.5 2.0data.frame(1:length(random.number),random.number,round(x,1))## X1.length.random.number. random.number round.x..1.
## 1 1 0.78809107 4.7
## 2 2 0.85113835 5.7
## 3 3 0.83712785 5.4
## 4 4 0.39579685 1.5
## 5 5 0.49387040 2.0
## 6 6 0.58318739 2.6
## 7 7 0.89667250 6.8
## 8 8 0.27145359 1.0
## 9 9 0.07705779 0.2
## 10 10 0.45359019 1.8
## 11 11 0.81436077 5.1
## 12 12 0.17863398 0.6
## 13 13 0.65323993 3.2
## 14 14 0.10332750 0.3
## 15 15 0.78108581 4.6
## 16 16 0.63047285 3.0
## 17 17 0.88616462 6.5
## 18 18 0.94045534 8.5
## 19 19 0.15061296 0.5
## 20 20 0.77057793 4.4
## 21 21 0.29947461 1.1
## 22 22 0.95971979 9.6
## 23 23 0.25744308 0.9
## 24 24 0.63572680 3.0
## 25 25 0.55166375 2.4
## 26 26 0.90367776 7.0
## 27 27 0.49211909 2.0
## 28 28 0.02802102 0.1
## 29 29 0.90893170 7.2
## 30 30 0.15761821 0.5
## 31 31 0.99124343 14.2
## 32 32 0.25043783 0.9
## 33 33 0.41506130 1.6
## 34 34 0.60070053 2.8
## 35 35 0.44833625 1.8
## 36 36 0.14886165 0.5
## 37 37 0.21541156 0.7
## 38 38 0.31173380 1.1
## 39 39 0.84588441 5.6
## 40 40 0.17162872 0.6
## 41 41 0.43257443 1.7
## 42 42 0.15236427 0.5
## 43 43 0.32574431 1.2
## 44 44 0.28721541 1.0
## 45 45 0.07355517 0.2
## 46 46 0.34325744 1.3
## 47 47 0.83887916 5.5
## 48 48 0.95096322 9.0
## 49 49 0.48161121 2.0
## 50 50 0.69702277 3.6
## 51 51 0.98248687 12.1
## 52 52 0.47460595 1.9
## 53 53 0.47635727 1.9
## 54 54 0.03152364 0.1
## 55 55 0.01926445 0.1
## 56 56 0.13309982 0.4
## 57 57 0.21891419 0.7
## 58 58 0.42206655 1.6
## 59 59 0.82136602 5.2
## 60 60 0.39929947 1.5
## 61 61 0.60420315 2.8
## 62 62 0.55866900 2.5
## 63 63 0.12434326 0.4
## 64 64 0.44308231 1.8
## 65 65 0.48336252 2.0
## 66 66 0.25218914 0.9
## 67 67 0.97022767 10.5
## 68 68 0.58844133 2.7
## 69 69 0.56217163 2.5
## 70 70 0.23467601 0.8
## 71 71 0.41856392 1.6
## 72 72 0.71103327 3.7
## 73 73 0.42381786 1.7
## 74 74 0.37653240 1.4
## 75 75 0.38704028 1.5
## 76 76 0.71803853 3.8
## 77 77 0.64448336 3.1
## 78 78 0.32749562 1.2
## 79 79 0.84238179 5.5
## 80 80 0.06129597 0.2
## 81 81 0.45709282 1.8
## 82 82 0.92469352 7.8
## 83 83 0.15411559 0.5
## 84 84 0.88091068 6.4
## 85 85 0.27495622 1.0
## 86 86 0.18739054 0.6
## 87 87 0.42907180 1.7
## 88 88 0.04203152 0.1
## 89 89 0.35026270 1.3
## 90 90 0.05954466 0.2
## 91 91 0.90192644 7.0
## 92 92 0.93695271 8.3
## 93 93 0.04028021 0.1
## 94 94 0.79509632 4.8
## 95 95 0.07180385 0.2
## 96 96 0.78809107 4.7
## 97 97 0.85113835 5.7
## 98 98 0.83712785 5.4
## 99 99 0.39579685 1.5
## 100 100 0.49387040 2.0hist(x, breaks = 20, probability = TRUE, col = "lightblue", ylim=c(0,0.35), main = "Distribución Exponencial (beta = 3)", xlab = "Valor de X")
# Añadimos la función de densidad teórica de la distribución exponencial
curve(dexp(x,rate=1/3), col = "red", lwd = 2, add = TRUE)
Simulando variables aleatorias con funciones propias de R
- Generando variable aleatoria uniforme
# Generar 100 variables aleatorias uniformes entre 0 y 1
uniform_random_01 <- runif(100, min = 0, max = 1)
uniform_random_01## [1] 0.117289396 0.365972482 0.824249706 0.927057338 0.023131659 0.587131697
## [7] 0.724494472 0.313586763 0.024436612 0.336643375 0.098467137 0.006268510
## [13] 0.982484703 0.833955415 0.473070601 0.910715278 0.289403163 0.855380691
## [19] 0.745682071 0.340948271 0.240078410 0.131008478 0.697750445 0.250937134
## [25] 0.144181756 0.714380757 0.293095027 0.056602086 0.146625306 0.311895784
## [31] 0.115420209 0.187422403 0.811353221 0.962640708 0.053512203 0.687762202
## [37] 0.491192857 0.887221866 0.262837145 0.655092454 0.916079981 0.008844472
## [43] 0.707630428 0.193260403 0.230552358 0.896569551 0.738976497 0.235488945
## [49] 0.004446825 0.880983622 0.888193806 0.031350792 0.311076592 0.607928958
## [55] 0.396468570 0.515957972 0.654968763 0.573244662 0.849717282 0.235926462
## [61] 0.740773789 0.195076687 0.993624196 0.013891154 0.416289991 0.234281247
## [67] 0.202382876 0.280712435 0.384511335 0.818597963 0.791658716 0.354953505
## [73] 0.609500641 0.171517182 0.461577187 0.888398775 0.910972387 0.645607931
## [79] 0.770445772 0.129754522 0.462990786 0.408264990 0.162542540 0.006298826
## [85] 0.171138217 0.301350780 0.489242328 0.856228312 0.075623884 0.691785644
## [91] 0.501499857 0.151456411 0.652880448 0.714240357 0.306979012 0.560440174
## [97] 0.731973997 0.049861196 0.153548159 0.838015049- Generando una variable aleatoria de una distribucion normal
# Generar 100 variables normales con media 30 y desviación estándar 3.5
set.seed(123)
normal_random <- rnorm(100, mean = 30, sd = 3.5)
normal_random## [1] 28.03834 29.19438 35.45548 30.24678 30.45251 36.00273 31.61321 25.57229
## [9] 27.59602 28.44018 34.28429 31.25935 31.40270 30.38739 28.05456 36.25420
## [17] 31.74248 23.11684 32.45475 28.34523 26.26262 29.23709 26.40898 27.44888
## [25] 27.81236 24.09657 32.93225 30.53681 26.01652 34.38835 31.49262 28.96725
## [33] 33.13294 33.07347 32.87553 32.41024 31.93871 29.78331 28.92913 28.66835
## [41] 27.56853 29.27229 25.57111 37.59135 34.22787 26.06912 28.58990 28.36671
## [49] 32.72988 29.70821 30.88661 29.90009 29.84995 34.79011 29.20980 35.30765
## [57] 24.57937 32.04615 30.43349 30.75580 31.32874 28.24187 28.83377 26.43499
## [65] 26.24873 31.06235 31.56873 30.18551 33.22794 37.17530 28.28139 21.91791
## [73] 33.52008 27.51780 27.59197 33.58950 29.00329 25.72749 30.63456 29.51388
## [81] 30.02017 31.34848 28.70269 32.25532 29.22830 31.16124 33.83894 31.52314
## [89] 28.85924 34.02083 33.47726 31.91939 30.83556 27.80233 34.76228 27.89909
## [97] 37.65567 35.36414 29.17505 26.40753hist(normal_random, col=3,main="Grafico de la normal",xlim=c(15,45))
- Generando Variable aleatoria de una exponencial
Para generar variables aleatorias con distribución exponencial con parámetro β . Ejemplo:
Simular El tiempo de servicio en una caja de un banco cuyo comportamiento sigue una forma exponencial con media de 3 minutos/cliente
numexp <- rexp(100,1/3)
round(numexp,1)## [1] 5.4 0.1 3.9 0.6 5.3 5.3 2.5 1.0 9.9 1.2 3.3 4.0 1.9 0.6 1.4
## [16] 1.1 10.4 3.8 3.2 0.9 0.3 8.9 5.9 2.0 4.8 1.8 0.3 1.0 5.3 0.8
## [31] 2.8 1.3 3.9 0.5 4.7 0.2 1.7 5.2 3.9 3.9 1.4 0.1 0.5 3.1 3.2
## [46] 11.2 0.0 0.0 5.1 1.6 1.4 0.2 1.6 4.4 3.8 4.3 6.4 4.3 2.7 8.1
## [61] 6.7 1.6 0.2 1.7 13.1 1.7 1.4 0.8 2.1 2.9 2.2 3.3 1.8 2.0 4.9
## [76] 0.3 1.2 8.2 8.6 0.1 1.4 2.0 2.9 3.4 0.2 4.9 0.5 4.4 0.3 0.7
## [91] 2.0 4.6 5.8 0.6 1.7 5.8 10.8 4.0 2.8 0.9- Generando variable aleatoria de una Binomial.
binom_random <- rbinom(100,12,0.25)
binom_random## [1] 6 4 2 2 6 3 4 2 4 3 5 2 2 1 2 6 2 4 4 1 2 2 1 1 7 7 1 5 3 3 3 4 1 2 4 2 4
## [38] 2 3 2 2 6 2 3 0 3 3 3 2 5 3 2 2 2 5 6 3 2 7 2 3 1 3 1 2 2 3 4 2 5 4 4 1 2
## [75] 4 3 2 2 1 2 5 4 4 1 1 6 3 3 2 3 2 2 4 2 4 1 4 3 4 4- Generando variables aletorias de una Poisson.
Supongamos que queremos simular un proceso donde los eventos ocurren con una tasa de λ=5 eventos por minuto .
evento_pois <- rpois(100,5)
tablapois <- table(evento_pois)
tablapois## evento_pois
## 0 1 2 3 4 5 6 7 8 9 10 11
## 1 4 10 8 19 17 14 14 5 5 1 2