This calculator allows you to calculate the probability of a beta distribution falling within a specified range or being greater than or less than a certain value. Simply input the alpha (α) and beta (β) parameters, the comparison type, and the value(s) to compare against to get started.
Definition: The beta distribution is a family of continuous probability distributions defined on the interval [0, 1]. It is characterized by two positive shape parameters, α (alpha) and β (beta), which determine its shape and properties.
Where:
library(tidyverse)
alpha <- 2
beta <- 5
# Calculate probability between two values
x1 <- 0.2
x2 <- 0.6
prob <- pbeta(x2, shape1 = alpha, shape2 = beta) - pbeta(x1, shape1 = alpha, shape2 = beta)
print(str_glue("P({x1} < X < {x2}) = {round(prob, 4)}"))
# P(0.2 < X < 0.6) = 0.6144import numpy as np
import pandas as pd
from scipy import stats
alpha = 2
beta = 5
# Calculate probability between two values
x1, x2 = 0.2, 0.6
prob = stats.beta.cdf(x2, alpha, beta) - stats.beta.cdf(x1, alpha, beta)
print(f"P({x1} < X < {x2}) = {prob:.4f}")This calculator allows you to calculate the probability of a beta distribution falling within a specified range or being greater than or less than a certain value. Simply input the alpha (α) and beta (β) parameters, the comparison type, and the value(s) to compare against to get started.
Definition: The beta distribution is a family of continuous probability distributions defined on the interval [0, 1]. It is characterized by two positive shape parameters, α (alpha) and β (beta), which determine its shape and properties.
Where:
library(tidyverse)
alpha <- 2
beta <- 5
# Calculate probability between two values
x1 <- 0.2
x2 <- 0.6
prob <- pbeta(x2, shape1 = alpha, shape2 = beta) - pbeta(x1, shape1 = alpha, shape2 = beta)
print(str_glue("P({x1} < X < {x2}) = {round(prob, 4)}"))
# P(0.2 < X < 0.6) = 0.6144import numpy as np
import pandas as pd
from scipy import stats
alpha = 2
beta = 5
# Calculate probability between two values
x1, x2 = 0.2, 0.6
prob = stats.beta.cdf(x2, alpha, beta) - stats.beta.cdf(x1, alpha, beta)
print(f"P({x1} < X < {x2}) = {prob:.4f}")