Korean, Edit

Chapter 7. Continuous Probability Distribution

Higher category : 【Statistics】 Statistics Overview

1. uniform distribution

2. normal distribution 

3. gamma distribution 

4. exponential distribution

5. beta distribution 

6. Pareto distribution 

7. logistic distribution

8. Dirichlet distribution

a. Q-Q plot

1. uniform distribution

⑴ definition: probability distribution with a constant probability for all random variables

⑵ probability density function: X ~ u[a, b], p(x) = 1 / (b - a) I｛a ≤ x ≤ b｝ 

① Python programming: Bokeh is used for web-page visualization 

from bokeh.plotting import figure, output_file, show

output_file("uniform_distribution.html")
p = figure(width=400, height=400, title = "Uniform Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line([1, 2, 3, 4, 5, 6, 7, 8, 9], [1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8], 
       line_width=2)
show(p)

⑶ statistics

① moment generating function

② average : E(X) = (a + b) / 2

③ variance: VAR(X) = (b - a)2 / 12

⑷ note

① marginal probability distribution has the meaning of length ÷ total area

2. normal distribution 

⑴ definition : the limit of _nC_x θ^x (1 - θ)^n-x by n → ∞

① as it is universally observed, it is called normal distribution

② generally, the standard normal distribution density function is expressed as φ(·) and the cumulative distribution function as Φ(·)

③ central limit theorem: if X = ∑X_i, taking n → ∞ will lead us to the normal distribution

④ first induced to approximate binomial distribution (De Moivre, 1721)

⑤ used to analyze model error in astronomy (Gaus, 1809)

○ by the fact, this is also known as Gaussian distribution

⑵ probability density function

① Python programming: Bokeh is used for web-page visualization 

# see https://stackoverflow.com/questions/10138085/how-to-plot-normal-distribution
import numpy as np
import scipy.stats as stats
from bokeh.plotting import figure, output_file, show

output_file("normal_distribution.html")
x = np.linspace(-3, 3, 100)
y = stats.norm.pdf(x, 0, 1)

p = figure(width=400, height=400, title = "Normal Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line(x, y, line_width=2)
show(p)

⑶ statistics

① moment generating function

② average : E(X) = μ

③ variance : VAR(X) = σ²

⑷ characteristic

① characteristic 1. symmetric around μ

② characteristic 2. if X ~ N(μ, σ²), Y = aX + b ~ N(aμ + b, a²σ²)

③ characteristic 3. if X_i ~ N(μ_i, σ_i²), X = ∑X_i ~ N(∑μ_i, ∑σ_i²)

④ characteristic 4. uncorrelatedness : if X and Y are jointly normal and uncorrelated, X and Y are independent

⑸ standard normal distribution 

① definition: a normal distribution with a mean of 0 and a standard deviation of 1

② normalization: if X ~ N(μ, σ²), Z = (X - μ) / σ

③ cumulative distribution function Φ(z) of the standard normal distribution 

④ z_α : z_α value is the value where the probability that X has a greater value than z_α is α

⑹ normal distribution table 

3. gamma distribution 

⑴ gamma function

① definition 1. for x ＞ 0, 

② definition 2. 

③ characteristic

○ Γ(-3/2) = 4/3 √π

○ Γ(-1/2) = -2 √π 

○ Γ(1/2) = √π 

○ Γ(1) = 1

○ Γ(3/2) = 1/2 √π

○ Γ(a + 1) = aΓ(a)

○ Γ(n + 1) = n! 

⑵ gamma distribution

① probability density function: for x, r, λ ＞ 0, 

○ Python programming: Bokeh is used for web-page visualization 

# see https://www.statology.org/gamma-distribution-in-python/

import numpy as np
import scipy.stats as stats
from bokeh.plotting import figure, output_file, show

output_file("gamma_distribution.html")
x = np.linspace(0, 40, 100)
y1 = stats.gamma.pdf(x, a = 5, scale = 3)
y2 = stats.gamma.pdf(x, a = 2, scale = 5)
y3 = stats.gamma.pdf(x, a = 4, scale = 2)

p = figure(width=400, height=400, title = "Normal Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line(x, y1, line_width=2, color = 'red', legend_label = 'shape=5, scale=3')
p.line(x, y2, line_width=2, color = 'green', legend_label = 'shape=2, scale=5')
p.line(x, y3, line_width=2, color = 'blue', legend_label = 'shape=4, scale=2')

show(p)

② meaning

○ the probability distribution of time until the r-th event occurs

○ r (shape parameter)

○ λ (rate parameter): the average number of events per unit period

○ β (scale paramete): β = 1 / λ

⑶ statistics

① moment generating function

② average : E(X) = r / λ 

③ variance: VAR(X) = r / λ²

⑷ relationship with different probability distributions

①  binomial distribution

② negative binomial distribution 

③ beta distribution

4. exponential distribution

⑴ Overview

① definition: a special case where α = 1 in the gamma distribution 

○ That is, the period until the first event occurs

② Special case with α = 1 in gamma distribution

③ meaning of parameter

○ β (survival parameter

○ λ (rate parameter): average number of events per unit period

④ Poisson distribution : duration is fixed. number of events is the random variable

⑵ probability density function: for x ＞ 0, 

① Python programming : Bokeh is used for web-page visualization 

# see https://www.alphacodingskills.com/scipy/scipy-exponential-distribution.php

import numpy as np
from scipy.stats import expon
from bokeh.plotting import figure, output_file, show

output_file("exponential_distribution.html")
x = np.arange(-1, 10, 0.1)
y = expon.pdf(x, 0, 2)

p = figure(width=400, height=400, title = "Exponential Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line(x, y, line_width=2, legend_label = 'loc=0, scale=2')

show(p)

⑶ statistics

① moment generating function

② average: E(X) = 1 / λ

○ meaning: intuitively, 1 / λ can be seen

③ variance: VAR(X) = 1 / λ²

⑷ memorylessness

① definition

② example: when battery life time follows exponential distribution, existing usage time doesn’t affect the remaining life time

5. beta distribution 

⑴ beta function: for α, β ＞ 0, 

⑵ beta distribution

① Python programming: Bokeh is used for web-page visualization 

# see https://vitalflux.com/beta-distribution-explained-with-python-examples/

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import beta
from bokeh.plotting import figure, output_file, show

output_file("beta_distribution.html")
x = np.linspace(0, 1, 100)
y1 = beta.pdf(x, 2, 8)
y2 = beta.pdf(x, 5, 5)
y3 = beta.pdf(x, 8, 2)

p = figure(width=400, height=400, title = "Beta Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line(x, y1, line_width=2, color = 'red', legend_label = 'a=2, b=8')
p.line(x, y2, line_width=2, color = 'green', legend_label = 'a=5, b=5')
p.line(x, y3, line_width=2, color = 'blue', legend_label = 'a=8, b=2')

show(p)

② E(X) = α ÷ (α + β) 

③ VAR(X) = αβ ÷ ((α + β)²(α + β + 1))

⑵ relationship with gamma function

⑶ characteristic

① commutative law: B(α, β) = B(β, α) 

② equivalent expression

⑷ generalized beta distribution

6. Pareto distribution

⑴ simple Pareto distribution

① probability density function: for shape parameter a, 

○ Python programming: Bokeh is used for web-page visualization   

# see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pareto.html

import matplotlib.pyplot as plt
from scipy.stats import pareto
from bokeh.plotting import figure, output_file, show

output_file("pareto_distribution.html")
x = np.linspace(1, 10, 100)
y1 = pareto.pdf(x, 1)
y2 = pareto.pdf(x, 2)
y3 = pareto.pdf(x, 3)

p = figure(width=400, height=400, title = "Pareto Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line(x, y1, line_width=2, color = 'red', legend_label = 'a=1')
p.line(x, y2, line_width=2, color = 'green', legend_label = 'a=2')
p.line(x, y3, line_width=2, color = 'blue', legend_label = 'a=3')

show(p)

② probability distribution function

⑵ generalized Pareto distributino

① probability density function: for scale parameter b,

② probability distribution function 

7. logistic distribution

⑴ simple logistic distribution

① probability density function

○ Python programming: Bokeh is used for web-page visualization 

#see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.logistic.html

import matplotlib.pyplot as plt
from scipy.stats import logistic
from bokeh.plotting import figure, output_file, show

output_file("logistic_distribution.html")
x = np.linspace(1, 10, 100)
y = logistic.pdf(x)

p = figure(width=400, height=400, title = "Logistic Distribution", 
           tooltips=[("x", "$x"), ("y", "$y")])
p.line(x, y, line_width=2)

show(p)

⑵ generalized logistic distribution

① probability density function

8. Dirichlet distribution

⑴ Overviwe : Drawing attention for being able to analyze the simplex

⑵ Probability density function : for x = (x₁, ····, x_D) and positive parameters (λ₁, ····, λ_D),

Input : 2019.06.19 00:27