# Probability (Random Variable)

## Previous Chapter

Introduction to Probability

# Concepts

## Random Variable

• Discrete
• Continuous

Guess which

• No. of people dying each day
• Number of heads in successive tosses.
• Heights of people in Bangladesh
• GPA of students
• Grade of students in individual subjects
• Income tax paid by people

## Integration

• $$\int x^3+2x$$
• $$\int_2^3 x^2+x$$
• Relationship between integration (I) and differentiation(D).

# Example

Results of an unbiased die throw

x 1 2 3 4 5 6
P $$\frac 1 6$$ $$\frac 1 6$$ $$\frac 1 6$$ $$\frac 1 6$$ $$\frac 1 6$$ $$\frac 1 6$$

Biased

x 1 2 3 4 5 6
P $$\frac 1 7$$ $$\frac 2 7$$ $$\frac 1 2$$ $$\frac 1 7$$ $$\frac 1 7$$ $$\frac 1 7$$

## Number of Heads in Coin Toss

Two coins are tossed successively.

N:B: X is variable & x is value

X = number of heads in the coin toss

x = 0, 1, 2

• P(x=0) = ?
• P(x=1)=?, P(x=2)=?

## Probability Function

Given, $$P(x) = \frac{2x+k}{56}; x = -3, -2, -1, 0, 1, 2, 3$$

Discrete or Continuous?

1. k = ?
2. Find probability of each value of x
3. Find $$P(-2 \le x \le 2)$$

Clue: If $$S=\{H,T\}, P(H) + P(T) = 1$$,

i.e. $$\sum$$ P(All possible values) = 1

## pdf and pmf

Probability Density Function (continuous)

• $$f\left( x \right) \ge 0$$ for all x
• $$\displaystyle \int_{{\, - \infty }}^{{\,\infty }}{{f\left( x \right)\,dx}} = 1$$
• $$P\left( {a \le X \le b} \right) = \int_{{\,a}}^{{\,b}}{{f\left( x \right)\,dx}}$$
• $$P(X=x)=0$$ (theoretically, why?)

Probability Mass Function (discrete)

• $$P\left( x \right) \ge 0$$ for all x
• $$\displaystyle \sum_{{\, - \infty }}^{{\,\infty }}{{f\left( x \right)\,dx}} = 1$$
• $$P_x(X) = P(X=x)$$

## Pdf problem 01

$$f\left( x \right) = \frac{{{x^3}}}{{5000}}\left( {10 - x} \right)$$ $$for 0 \le x \le 10$$ and $$f\left( x \right) = 0$$ for all other values.

1. Show that $$f\left( x \right)$$ is a probability density function.
2. Find $$P\left( {1 \le X \le 4} \right)$$
3. Find $$P\left( {x \ge 6} \right)$$
• is it $$\displaystyle \int_{7}^{10} f(x) dx$$?

## Pdf problem 02

$$f(x) = k(x+1); 0\lt x \lt 1$$

1. $$P(X=2)=?$$
2. $$k=?$$
3. $$P(0.4 \lt X \lt 2)=?$$
• $$\int_{0.4}^1 f(x) + \int_{1}^2 f(x) \to 0$$

## Cumuluative Distribution Function

F(x) or cdf accumulates all of the probability less than or equal to.

x 1 2 3 4 5 6
P (x) $$\frac 1 7$$ $$\frac 1 7$$ $$\frac 1 2$$ $$\frac 1 7$$ $$\frac 1 7$$ $$\frac 1 7$$
F (x) $$\frac 1 7$$ $$\frac 2 7$$ $$\frac 4 7$$ $$\frac 5 7$$ $$\frac 6 7$$ $$1$$

Find

1. $$P(X<4)$$
2. $$P(3<X<6)$$

## cdf definition

$$F_X(x) = P(X\le x)$$

Discrete

$F(x) = \sum_{X\le x} P(x)$

Continuous

• $F_{X}(x) = \int_{-\infty}^x f_X(t)dt$
• Find cdf for $$f(x) = 2x; 0\le x \le 1$$
• $$\int \to x^2$$

## cdf properties

• $$P(a\le x \le b) = F(b)-F(a)$$for $$a\lt b$$; what if $$a \lt x \lt b?$$
• For continuous x, $$f(x) = \frac{d}{dx}[F(x)]$$
• $$F(-\infty) =0 , F(+\infty) = 1$$

## Joint Probability Function

Let, $$I = Infected$$, and $$V = Vaccinated$$

$$I$$ $$\bar I$$ Total
$$V$$ 3 276 279
$$\bar V$$ 66 473 539
Total 69 749 818

Find the probability that

1. a vaccinated person is infected
2. a non-vaccinated person is uninfected
• These are joint probabilities $$\to$$ P(x,y)
• $$P(x,y) =P(x) \cdot P(y)$$ if $$x$$ and $$y$$ are independent.

## Joint-Marginal-Conditional

Let, $$I = Infected$$, and $$V = Vaccinated$$

$$I$$ $$\bar I$$ Total
$$V$$ 3 276 279
$$\bar V$$ 66 473 539
Total 69 749 818
Find the probabilities that
1. a vaccinated person is infected
1. a non-vaccinated person is uninfected
1. vaccinated if infected
1. infected if not vaccinated
1. vaccinated
1. uninfected

## Joind PF Properties

• $$P(x,y) \ge 0$$
• $$\Sigma\Sigma P(x,y)=1$$

## Coin-Die

Die/Coin 1 2 3 4 5 6
H (1) H1 H2 H3 H4 H5 H6
T (0) T1 T2 T3 T4 T5 T6

X = Outcome of coin toss

Y = Outcome of die throw

x = 0, 1; y = 1, 2, 3, 4, 5, 6

Construct the distribution.

## Joint-Marginal-Conditional Revisited

Exam (X) $$\to$$
Result (Y) $$\downarrow$$
PSC JSC SSC HSC Total
Passed 30 26 23 25 104
Failed 12 13 10 14 49
Absent 5 2 3 4 14
Total 47 41 36 43 167
• Marginal: $$P(Pass) = P(x_1)=P(x_1,y_1)+P(x_1,y_2)+P(x_1,y_3)$$
• $$P(Absent) = P(x_3) = P(x_3,y_1)+P(x_3,y_2)+P(x_3,y_3)$$

## Marginal Probability

Consider the previous table

Joint probability: $$P(x_i, y_j); i = 1,2, \cdots m; j = 1,2, \cdots n$$

Marginal probability $$\to P(x_i) \leftarrow P(x_i, y_j)$$

• For x: $$\displaystyle P(x_i) = \sum_{j=1}^n P(x_i, y_j); i = 1,2, \cdots m$$
• For y: $$\displaystyle P(y_i) = \sum_{i=1}^m P(x_i, y_j); j = 1,2, \cdots n$$

## Marginal Probability Properties

• $$P(x_i) \ge 0$$ and $$P(y_i) \ge 0$$
• $\sum_{i=1}^m P(x_i)=\sum_{j=1}^n P(y_j)=1$

Summing marginal probabilities will give 1.

## Joint-Marginal Example

$$P(x,y) = \frac{x+y}{9}; x=0,1,2; y = 0, 1$$

• Find marginal probabilities
• Check properties (sum)
• $$P(x) = \frac{2x+1}{9}$$

## Conditional Probability Function

Like Bayes Theorem

$$P(X_i|y_j) = \frac{P(x_i,y_j)}{P(y_j)}; P(y_j) \gt 0$$

Properties

• $$\sum_{j=1}^m P(x_i|y_j)=\sum_{i=1}^m P(y_j|x_i)=1$$

## Conditional Probability Example

$$P(x,y) = \frac{x+y}{9}; x=0,1,2; y = 0, 1$$

Find $$P(X|Y)$$ and $$P(Y|X)$$

Find for continuous X as well.

## Find k for pdf

$$f(x) = kx^2+kx+\frac 1 8; 0 \lt x \lt 2$$

1. Find k
2. Find $$P(1 \lt X \lt 2)$$

## Next Chapter

Mathematical Expectation