# Beta Type-I Distribution

## Beta Type-I Distribution

A continuous random variable $X$ is said to have a beta type-I distribution with parameters $m$ and $n$ if its p.d.f. is given by

 \begin{align*} f(x)&= \begin{cases} \frac{1}{B(m,n)}x^{m-1}(1-x)^{n-1}, & 0\leq x\leq 1;m,n>0 \\ 0, & Otherwise. \end{cases} \end{align*}

where $B(m,n) =\frac{\Gamma m \Gamma n}{\Gamma (m+n)}$. In notation, it can be written as $X\sim \beta_1(m,n)$.

## Mean and Variance of Beta Type-I distiribution

The mean and variance of Beta Type-I distribution are $\mu_1^\prime =\frac{m}{(m+n)}$ and $\mu_2 = \frac{mn}{(m+n)^2(m+n+1)}$ respectuvely.

#### Proof

The mean of Beta Type-I distribution is

 $$\begin{eqnarray*} \text{mean = }\mu_1^\prime &=& E(X) \\ &=& \int_{0}^1 x\frac{1}{B(m,n)} x^{m-1}(1-x)^{n-1}\; dx\\ &=& \frac{1}{B(m,n)} \int_{0}^1 x^{m+1-1}(1-x)^{n-1}\; dx\\ &=& \frac{1}{B(m,n)} B(m+1, n)\\ &=& \frac{B(m+1, n)}{B(m,n)} \\ &=&\frac{\Gamma (m+1) \Gamma n}{\Gamma (m+n+1)}\cdot \frac{\Gamma(m+n)}{\Gamma m \Gamma n}\\ &=&\frac{m\Gamma m \Gamma n}{(m+n)\Gamma (m+n)}\cdot \frac{\Gamma(m+n)}{\Gamma m \Gamma n}\\ &=&\frac{m}{(m+n)} \end{eqnarray*}$$
To find variance of $X$, we need to find the $E(X^2)$.

 $$\begin{eqnarray*} \mu_2^\prime&= &E(X^2)\\ &=& \int_{0}^1 x^2\frac{1}{B(m,n)} x^{m-1}(1-x)^{n-1}\; dx\\ &=& \frac{1}{B(m,n)} \int_{0}^1 x^{m+2-1}(1-x)^{n-1}\; dx\\ &=& \frac{1}{B(m,n)} B(m+2, n)\\ &=& \frac{B(m+2, n)}{B(m,n)} \end{eqnarray*}$$

 $$\begin{eqnarray*} \mu_2^\prime &=&\frac{\Gamma (m+2) \Gamma n}{\Gamma (m+n+2)}\cdot \frac{\Gamma(m+n)}{\Gamma m \Gamma n}\\ &=&\frac{(m+1)m\Gamma m \Gamma n}{(m+n+1)(m+n)\Gamma (m+n)}\cdot \frac{\Gamma(m+n)}{\Gamma m \Gamma n}\\ &=&\frac{m(m+1)}{(m+n)(m+n+1)}. \end{eqnarray*}$$

Hence, the variance of Beta Type-I distribution is

 $$\begin{eqnarray*} \text{Variance = } \mu_2&=&\mu_2^\prime-(\mu_1^\prime)^2\\ &=&\frac{m(m+1)}{(m+n)(m+n+1)}-\bigg(\frac{m}{(m+n)}\bigg)^2\\ &=&\frac{m}{(m+n)}\bigg[\frac{m+1}{m+n+1}-\frac{m}{m+n}\bigg]\\ &=&\frac{m}{(m+n)}\bigg[\frac{m^2+mn+m+n -m^2-mn-m}{(m+n+1)(m+n)}\bigg]\\ &=&\frac{mn}{(m+n)^2(m+n+1)}. \end{eqnarray*}$$

Hence the mean and variance of Beta Type-I distribution are $\mu_1^\prime =\frac{m}{(m+n)}$ and $\mu_2 = \frac{mn}{(m+n)^2(m+n+1)}$ respectively.

## Harmonic Mean

Let $H$ be the harmonic mean of Beta Type I distribution. Then the harmonic mean of Beta Type-I distribution is $H = \frac{m-1}{m+n-1}$.

#### Proof

 $$\begin{eqnarray*} \frac{1}{H} &=& E(1/X) \\ &=& \int_{0}^1 \frac{1}{x}\frac{1}{B(m,n)} x^{m-1}(1-x)^{n-1}\; dx\\ &=& \frac{1}{B(m,n)} \int_{0}^1 x^{m-1-1}(1-x)^{n-1}\; dx\\ &=& \frac{1}{B(m,n)} B(m-1, n)\\ &=& \frac{B(m-1, n)}{B(m,n)} \\ &=&\frac{\Gamma (m-1) \Gamma n}{\Gamma (m+n-1)}\cdot \frac{\Gamma(m+n)}{\Gamma m \Gamma n}\\ &=&\frac{\Gamma (m-1) \Gamma n}{\Gamma (m+n-1)}\cdot \frac{(m+n-1)\Gamma(m+n-1)}{(m-1)\Gamma (m-1) \Gamma n}\\ &=&\frac{m+n-1}{m-1}\\ \therefore H &=& \frac{m-1}{m+n-1}. \end{eqnarray*}$$

Hence the mean of Beta Type-I distribution is $H =\frac{m-1}{m+n-1}$.

## Mode of Beta Type-I distribution

The mode of Beta Type-I distribution is $\frac{m-1}{m+n-2}$

#### Proof

The p.d.f. of Beta Type-I distribution is

 \begin{align*} f(x)&= \begin{cases} \frac{1}{B(m,n)}x^{m-1}(1-x)^{n-1}, & 0\leq x\leq 1;m,n>0 \\ 0, & Otherwise. \end{cases} \end{align*}

Taking $\log_e$, we get

 $$\begin{equation*} \log_e f(x)=-\log B(m,n) +(m-1)\log x+ (n-1)\log(1-x). \end{equation*}$$

Differentiating $\log f(x)$ w.r.t. $x$, we get

 $$\begin{eqnarray}\label{b11} \frac{d \log_e f(x)}{dx}& = & 0 +\frac{m-1}{x}+ \frac{n-1}{(1-x)}(-1)\\ & = & \frac{m-1}{x}- \frac{n-1}{(1-x)}. \end{eqnarray}$$

By the principal of maxima and minima,

 $$\begin{eqnarray*} & &\frac{d \log_e f(x)}{dx}=0 \\ \Rightarrow & & \frac{m-1}{x}- \frac{n-1}{(1-x)}=0\\ \Rightarrow & & (m-1)-x(m-1)= x(n-1)\\ \Rightarrow & & x(m+n-2) = (m-1)\\ \Rightarrow & & x_0=\frac{m-1}{m+n-2}. \end{eqnarray*}$$

Also,

 $$\begin{equation*} \frac{d^2 \log_e f(x)}{dx^2}\bigg|_{x=x_0}< 0. \end{equation*}$$

Hence, $f(x)$ is maximum at $x= x_0 = \frac{m-1}{m+n-2}$.

Therefore, mode of Beta Type-I distribution is $\frac{m-1}{m+n-2}$.

## Conclusion

In this tutorial, you learned about theory of Beta Type I distribution like the probability density function, mean, variance, harmonic mean and mode of Beta Type I distribution.

To read more about the step by step examples and calculator for Beta Type I distribution refer the link Beta Type I Distribution Calculator with Examples. This tutorial will help you to understand how to calculate mean, variance of Beta Type I distribution and you will learn how to calculate probabilities and cumulative probabilities for Beta Type I distribution with the help of step by step examples.
{"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0}