Binomial Distribution#

Motivation#

Y = \begin{array}{ c l }
    1                 & \quad \textrm{with probability} p \\
    0                 & \quad \textrm{with probability } 1 - p
\end{array}

TODO

Consider a random variable defined as the sum of n Bernoulli random variables,

X = Y_1 + Y_2 + ... + Y_{n-1} + Y_n

Where each Y_i takes the value 1 with probabilitiy p or it takes the value 0 with probabilitiy 1 - p.

TODO

From Conditional Probability the probability of an intersection of independent events is the product of individual probabilitiy,

P(A \cap B) = P(A) \cdot P(B)

TODO

Probabilitiy Distribution#

p(x; n, p) = C^{n}_x \cdot p^{x} \cdot (1 - p)^{n-x}

(Source code, png, hires.png, pdf)

../../_images/binomial_distribution_01.png

(Source code, png, hires.png, pdf)

../../_images/binomial_distribution_02.png

(Source code, png, hires.png, pdf)

../../_images/binomial_distribution_03.png

(Source code, png, hires.png, pdf)

../../_images/binomial_distribution_04.png