## "Or" Rule

The probability of Event A or Event B happens is the addition of P(A) and P(B) subtracted by the probability of event a and event b happens at the same time.

P(A or B) = P(A) + P(B) - P(A and B)

## "Multiplication Rule"

The probability of Event A and Event B happens at the same time is the product of the conditional probability of A given B and the probability of B.

P(A and B) = P(A/B) * P(B)

## The Law of Total Probability

The law of total probability is the proposition that if {Bn: n = 1, 2, 3, ...} is a finite or countably infinite partition of a sample space and each event Bn is measurable, then for any event A of the same probability space:

P(A) = SUM( P(A and Bx) ) x <- 0 to n

The law of total probability = "Or" Rule + "Multiplication Rule"

## Independence Test

Two events are independent if P(A and B) = P(A)*P(B)

## EM Algorithm

In the E-step, the missing data is estimated through the technique of conditional expectation. In the M-step, the non-hidden parameters are estimated through MLE.

- E-step

P(W0 | xi) = a, P(W1 | xi) = b

E(W) = a*W0 + b*W1

if |E(W) - W0| < |E(W) - W1|, then W = W0, else, W = W1.

## Conditional Expectation

E(Y | X = x) = Sum( y * P ( y | x ) )

## Covariance v.s. Correlation

Similar to : Variance v.s. Standard Deviation

## How to Estimate the Parameters of a Statistical Model

A statistical model can take the form of a explicit algebraic expressions with parameters.

Or a model can contain no algebraic expressions but only conditional/joint or other probability measurements (called free parameters). These probability measurements can be think as the sampling of the subject population.

The true value of the probability of event A: PA(X=a) can be estimated by repeat the random experiment (repeat the random process), PA(X=a) ~= ratio(A/all). The limit of this ratio is the true value of the probability of event A (happening).

## Distribution and Set

A bionomial random experiment contains several bornouli random experiments.

The subset of a sampling of certain distribution satisfy the same distribution.

## Random Variable, Probability and Distribution

Random variable and the probability of a random variable given certain value (an event) refers to a specific random experiment.

The distribution, in contrast, describe the subject in the overall trend (the population , the sample set).

## P(X) and P(X=x0)

P(X) is the PDF or PMF of a distribution. P(X=x0) is the probability of random variable X reach a value of x0.