@Lynxoria

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pareto

# Simuler la v.a. Pareto(α=9, β=2)
alpha1 = 9
beta1 = 2
Nmc = 10000
pareto_data1 = pareto.rvs(alpha1, size=Nmc, scale=beta1)

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import expon

# Fonction pour générer des variables aléatoires exponentielles
def generate_exponential_variables(lambda_val, Nmc):
    U = np.random.rand(Nmc)
    X = -1 / lambda_val * np.log(1 - U)
    return X

import matplotlib.pyplot as plt
import numpy as np

def simuler_geometrique(p):
    k = 0
    while np.random.rand() > p:
        k += 1
    return k

# Simulation de la variable aléatoire géométrique avec p = 0.2

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import binom
import random

###1###

# données

N_binomial = 20

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import poisson

###1###

# données

lmbda = 2  
Nmc = 10000