最近在學習貝葉斯方面的内容，有一個例子涉及到二項分布，然後還和beta函數，gamma函數有些關系，因為不太熟悉，是以整理一下。
B \Beta B函數
wiki: https://en.wikipedia.org/wiki/Beta_distribution
In Bayesian inference, the beta distribution is the conjugate(coupled, connected, or related) prior probability distribution for the Bernoulli, binomial, negative binomial and geometric distributions.
For example, the beta distribution can be used in Bayesian analysis to describe initial knowledge concerning probability of success such as the probability that a space vehicle will successfully complete a specified mission. The beta distribution is a suitable model for the random behavior of percentages and proportions.
The probability density function (pdf) of the beta distribution, for 0 ≤ x ≤ 1, and shape parameters α, β > 0, is a power function of the variable x and of its reflection (1 − x) as follows:
where Γ ( z ) \Gamma (z) Γ(z) is the gamma function. The beta function, B \Beta B, is a normalization constant to ensure that the total probability is 1. In the above equations x x x is a realization—an observed value that actually occurred—of a random process X X X.
Γ \Gamma Γ函數
wiki: https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the
gamma function(represented by
Γ, the capital Greek alphabet letter gamma) is an extension of the factorial function, with its argument shifted down by 1, to real and complex numbers. If n is a positive integer,
Γ \Gamma Γ= ( n − 1 ) ! (n-1)! (n−1)!
The gamma function is defined for all complex numbers except the non-positive integers. For complex numbers with a positive real part, it is defined via a convergent improper integral:
Γ \Gamma Γ與 B \Beta B函數的關系
推導參考: https://blog.csdn.net/xhf0374/article/details/53946146
結論：
Γ ( x ) = ∫ 0 + ∞ e − t t x − 1 d t \Gamma(x) =\int_0^{+\infty} e^{-t} t^{x-1} \mathrm{d}t Γ(x)=∫0+∞e−ttx−1dt
B ( x , y ) = ∫ 0 1 t x − 1 ( 1 − t ) y − 1 d t \mathrm{B} (x,y)=\int_0^1 t^{x-1}(1-t)^{y-1} \mathrm{d}t B(x,y)=∫01tx−1(1−t)y−1dt
B ( x , y ) = Γ ( x ) Γ ( y ) Γ ( x + y ) \mathrm{B}(x,y)=\frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)} B(x,y)=Γ(x+y)Γ(x)Γ(y)