RANDOM VARIABLES: $$ f:\Omega\mapsto\mathbb{R} $$
For a random variable $X$, the function $F$ defined by $$ F(x)=P{X \leq x} \qquad -\infty \leq x \leq \infty $$ is called the cumulative distribution function, or, more simply, the distribution function of $X$. Thus, the distribution function specifies, for all real values $x$, the probability that the random variable is less than or equal to $x$.
DISCRETE RANDOM VARIABLES
A random variable that can take on at most a countable number of possible values is said to be discrete. For a discrete random variable $X$, we define the probability mass function $p(a)$ of $X$ by $$ p(a)=P{X=a} $$ The probability mass function $p(a)$ is positive for at most a countable number of values of $a$. That is, if $X$ must assume one of the values $x_1, x2, \dots ,$ then
$p(x_i) \geq 0$ for $ i = 1, 2, \dots$ $p(x) = 0$ for all other values of $x$
Since $X$ must take on one of the values $x_i$, we have $$ \sum_{i=1}^{\infty}p(x_i)=1 $$
EXPECTED VALUE
If $X$ is a discrete random variable having a probability mass function $p(x)$, then the expectation,or the expected value of $X$, denoted by $\mathbb{E}[X]$, is defined by $$ \mathbb{E}[X]=\sum_{x:p(x)>0}xp(x) $$ In words, the expected value of $X$ is a weighted average of the possible values that $X$ can take on, each value being weighted by the probability that $X$ assumes it.
Remark. IF the expected number is larger than the average number
The probability concept of expectation is analogous to the physical concept of the center of gravity of a distribution of mass. Consider a discrete random variable $X$ having probability mass function $p(x_i),i\geq1$. If we now imagine a weightless rod in which weights with mass $p(x_i),i\geq1$, are located at the points $x_i,i\geq1$ , then the point at which the rod would be in balance is known as the center of gravity. For those readers acquainted with elementary statics, it is now a simple matter to show that this point is at $\mathbb{E}[X]$.
EXPECTATION OF A FUNCTION OF A RANDOM VARIABLE
Suppose $g$ is a function of a random variable, $$ X:\Omega\mapsto\Bbb{R} \ g:\Bbb{R}\mapsto\Bbb{R} $$ implies that $g \cdot X:\Omega\mapsto\Bbb{R}\mapsto\Bbb{R}$
Proposition 1.
If $X$ is a discrete random variable that takes on one of the values $x_i, i\geq1$, with respective probabilities $p(x_i)$, then, for any real-valued function $g$, $$ \mathbb{E}[g(X)]=\sum_{i}g(x_i)p(x_i) $$
Utility Function
Corollary 1.
If $a$ and $b$ are constants, then $$ \mathbb{E}[aX+b]=a\mathbb{E}[X]+b $$
Moment
The expected value of a random variable $X$, $\mathbb{E}[X]$, is also referred to as the mean or the first moment of $X$.
The quantity $\mathbb{E}[X_n], n \geq 1$, is called the $n$th moment of $X$. $$ E[X^n]=\sum_{x:p(x)>0}x^np(x) $$
VARIANCE
Because we expect $X$ to take on values around its mean $\mathbb{E}[X]$, it would appear that a reasonable way of measuring the possible variation of $X$ would be to look at how far apart $X$ would be from its mean, on the average. One possible way to measure this variation would be to consider the quantity $\mathbb{E}[|X − μ|]$, where $μ = \mathbb{E}[X]$. $$ Var(X)=\mathbb{E}[(X-\mu)^2] $$ The variance of $X$ is equal to the expected value of $X^2$ minus the square of its expected value. $$ Var(X)=\mathbb{E}[X^2]-(\mathbb{E}[X])^2 $$ If $a$ and $b$ are constants, then $$ Var(aX+b)=a^2Var(X) $$
Remarks.
(a) Analogous to the means being the center of gravity of a distribution of mass, the variance represents, in the terminology of mechanics, the moment of inertia.
(b) The square root of the $Var(X)$ is called the standard deviation of $X$, and we denote it by $SD(X)$. That is, $$ SD(X) = Var(X) $$
THE BERNOULLI AND BINOMIAL RANDOM VARIABLES
Suppose that a trial, whose outcome can be classified as either a success($X = 1$) or a failure($X = 0$) is performed.
The probability mass function of $X$ is given by $$ p(0)=P{X=0}=1-p \ p(1)=P{X=1}=p $$ where $p(0 \leq p \leq 1)$, is the probability that the trial is a success.
The random variable $X$ is said to be a Bernoulli random variable. $$ X\sim Ber(p) $$ means $X$ is distributed like a Bernoulli random variable.
Suppose now that $n$ independent trials, each of which results in a success with probability $p$ and in a failure with probability $1 − p$, are to be performed.
If $X$ represents the number of successes that occur in the $n$ trials, then $X$ is said to be a binomial random variable with parameters $(n, p)$.
Thus, a Bernoulli random variable is just a binomial random variable with parameters $(1, p)$.
The probability mass function of a binomial random variable having parameters $(n, p)$ is given by $$ p(i)=\binom{n}{i}p^{i}(1-p)^{n-i} $$ Note that, by the binomial theorem, the probabilities sum to $1$; that is, $$ \sum_{i=0}^{\infty}{p(i)}=\sum_{i=0}^{n}{\binom{n}{i}}{p^i}{(1-p)^{n-i}}=[p+(1-p)]^n=1 $$
Properties of Binomial Random Variables
If $X$ is a binomial random variable with parameters $n$ and $p$,then $$ \begin{aligned} \mathbb{E}[X]&=np \ Var(X)&=np(1-p) \end{aligned} $$ The following proposition details how the binomial probability mass function first increases and then decreases.
Proposition 2.
If $X$ is a binomial random variable with parameters $(n, p)$, where $0 < p < 1$, then as $k$ goes from $0$ to $n$, $P{X = k}$ first increases monotonically and then decreases monotonically, reaching its largest value when $k$ is the largest integer less than or equal to $[(n + 1)p]$.
Computing the Binomial Distribution Function
Suppose that $X$ is binomial with parameters $(n, p)$. The key to computing its distribution function $$ P{X\leq{i}}=\sum_{k=0}^{i}\binom{n}{k}p^{k}(1-p)^{n-k} $$ is to utilize the following relationship between $P{X = k + 1}$ and $P{X = k}$, which was established in the proof of Proposition 2: $$ P{X=k+1}=\frac{p}{1-p}\frac{n-k}{k+1}P{X=k} $$
THE POISSON RANDOM VARIABLE
A random variable $X$ that takes on one of the values $0, 1, 2, \dots$ is said to be a Poisson random variable with parameter $\lambda$ if, for some $\lambda > 0$, $$ p(i)=P{X=i}=e^{-\lambda}\frac{\lambda^i}{i!} $$ this defines a probability mass function since $$ \sum_{i=0}^{\infty}p(i)=e^{-\lambda}\sum_{i=0}^{\infty}\frac{\lambda^i}{i!}=e^{-\lambda}e^{\lambda}=1 $$ The Poisson random variable may be used as an approximation for a binomial random variable with parameters $(n, p)$ when $n$ is large and $p$ is small enough so that $np$ is of moderate size.
Expected value and Variance
The expected value and variance of a Poisson random variable are both equal to its parameter $\lambda$. $$ \begin{aligned} \mathbb{E}[X]&=\lambda \ Var(X)&=\lambda \end{aligned} $$ In fact, it remains a good approximation even when the trials are not independent, provided that their dependence is weak.
Poisson Paradigm.
Consider $n$ events, with $p_i$ equal to the probability that event $i$ occurs. If all the $p_i$ are “small” and the trials are either independent or at most “weakly dependent”, then the number of these events that occur approximately has a Poisson distribution with mean $\sum_{i=1}^n{p_i}$
- Length of the longest run
OTHER DISCRETE PROBABILITY DISTRIBUTIONS
The Geometric Random Variable
Suppose that independent trials, each having a probability $p,0 < p < 1$, of being a success, are performed until a success occurs. If we let $X$ equal the number of trials required, then
$$ P{X=n}=(1-p)^{n-1}p \qquad n=1,2,\dots $$
Since $$ \sum{P{X=n}}=p\sum{(1-p)^{n-1}}=\frac{p}{1-(1-p)}=1 $$ it follows that, with probability $1$, a success will eventually occur. Any random variable $X$ whose probability mass function is given by $(1-p)^{n-1}p$ is said to be a geometric random variable with parameter $p$.
Expected value and Variance $$ \begin{aligned} \mathbb{E}[X]&=1/p \ Var(X)&=\frac{1-p}{P^2} \end{aligned} $$
The Negative Binomial Random Variable
Suppose that independent trials, each having probability $p,0 < p < 1$, of being a success are performed until a total of $r$ successes is accumulated. If we let $X$ equal the number of trials required, then
$$ P{X=n}=\binom{r-1}{n-1}p^{r}(1-p)^{n-r} \qquad n=r,r+1,\dots $$
in order for the $r$th success to occur at the $n$th trial, there must be $r − 1$ successes in the first $n − 1$ trials and the $n$th trial must be a success. The probability of the first event is
$$ \binom{r-1}{n-1}p^{r-1}(1-p)^{n-r} $$
and the probability of the second is $p$; thus, by independence, the equation is established. To verify that a total of $r$ successes must eventually be accumulated, either we can prove analytically that
$$ \sum_{n=r}^{\infty}P{X=n}=\sum_{n=r}^{\infty}\binom{r-1}{n-1}p^{r}(1-p)^{n-r}=1 $$
or we can give a probabilistic argument as follows: The number of trials required to obtain $r$ successes can be expressed as $Y_1 + Y_2 + \cdots + Y_r$, where $Y_1$ equals the number of trials required for the first success, $Y_2$ the number of additional trials after the first success until the second success occurs, $Y_3$ the number of additional trials until the third success, and so on. Because the trials are independent and all have the same probability of success, it follows that $Y_1 + Y_2 + \cdots + Y_r$ are all geometric random variables. Hence, each is finite with probability $1$, so $\sum\limits_{i=1}^{r}Y_i$ must also be finite. Any random variable $X$ whose probability mass function is given by the equation is said to be a negative binomial random variable with parameters $(r, p)$. Note that a geometric random variable is just a negative binomial with parameter $(1, p)$.
Expected value and Variance $$ \begin{aligned} \mathbb{E}[X]&=\frac{r}{p}\ Var(X)&=\frac{r(1-p)}{p^2} \end{aligned} $$ Since a geometric random variable is just a negative binomial with parameter $r = 1$, it follows from the preceding example that the variance of a geometric random variable with parameter $p$ is equal to $(1 − p)/p^2$
The Hypergeometric Random Variable
$$ P{X=i}=\frac{\binom{m}{i}\binom{N-m}{n-i}}{\binom{N}{n}} \qquad i=0,1,\dots,n $$
Expected value and Variance $$ \begin{aligned} \mathbb{E}[X]&=\frac{nm}{N}\ Var(X)&=\frac{nm}{N}[\frac{(n-1)(m-1)}{N-1}+1-\frac{nm}{N}] \end{aligned} $$ Letting $p = m/N$ and using $$ \frac{m-1}{N-1}=\frac{Np-1}{N-1}=p-\frac{1-p}{N-1} $$ shows that $$ Var(X)=np(1-p)(1-\frac{n-1}{N-1}) $$
if $N$ is large in relation to $n$ [so that $(N − n)/(N − 1)$ is approximately equal to $1$], then $$ Var(x) \approx np(1-p) $$
The Zeta (or Zipf) Distribution
A random variable is said to have a zeta (sometimes called the Zipf) distribution if its probability mass function is given by $$ p{X=k}=\frac{C}{k^{\alpha+1}} \qquad k=1,2,\dots $$ for some value of $α> 0$. Since the sum of the foregoing probabilities must equal $1$, it follows that $$ C=\Bigg[\sum_{1}^{\infty}(\frac{1}{k})^{\alpha+1}\Bigg]^{-1} $$
EXPECTED VALUE OF SUMS OF RANDOM VARIABLES
Proposition 3.
$$ \mathbb{E}[X]=\sum_{s\in S}X(s)p(s) $$
Corollary 3.
For random variables $X_1,X_2, \dots ,X_n,$ $$ \mathbb{E}\bigg[\sum_{i=1}^{n}X_i\bigg]=\sum_{i=1}^{n}\mathbb{E}[X_i] $$
PROPERTIES OF THE CUMULATIVE DISTRIBUTION FUNCTION
For the distribution function $F$ of $X$, $F(b)$ denotes the probability that the random variable $X$ takes on a value that is less than or equal to $b$. Following are some properties of the cumulative distribution function (c.d.f.) $F$:
- $F$ is a nondecreasing function; that is, if $a < b$,then $F(a) \leq F(b)$.
- $\lim\limits_{b\to\infty}F(b)=1$
- $\lim\limits_{b\to-\infty}F(b)=0$
- $F$ is right continuous. That is, for any $b$ and any decreasing sequence $b_n, n \geq 1$, that converges to $b$, $\lim\limits_{n\to\infty}F(b_n)=F(b)$