Probability 1

The ASTA team

Introduction to probability

Events

Combining events

Probability of event

Probability of mutually exclusive events

\[ P(A\cup B) = P(A) + P(B). \]

\[P(A\cup B) = P(A) + P(B) = \tfrac{1}{6} + \tfrac{1}{6} = \tfrac{1}{3}. \]

Probability of union

\[ P(A\cup B) = P(A) + P(B) - P(A\cap B).\]

\[P(A\cup B) = P(A) + P(B) -P(A\cap B)= \tfrac{1}{3} + \tfrac{1}{3} - \tfrac{1}{6} = \tfrac{1}{2}.\]

Probability of complement

Conditional probability

Independent events

Independent events - equivalent definition

Stochastic variables

Definition of stochastic variables

Discrete or continuous stochastic variables

Discrete random variables

Discrete random variables

The distribution function

Mean of a discrete variable

Variance of a discrete variable

Continuous random variables

Distribution of continuous random variables

\[P(a<X<b) = \int_a^b f(x) dx.\]

Example: The uniform distribution

\[P\left(\tfrac{1}{3}<X\leq \tfrac{2}{3}\right) =P\left(\tfrac{1}{3}<X < \tfrac{2}{3}\right) + P\left(X = \tfrac{2}{3}\right)\\ = \int_{1/3}^{2/3}f(x)dx + 0 =\int_{1/3}^{2/3}1dx = \frac{1}{3}.\]

Density shapes

Distribution function of continuous variable

Mean and variance of a continuous variable

Example: Mean and variance in the uniform distribution

Rules for computing mean and variance