Probability (Probability) : numerical measure of the likelihood of the event occurring.
Combinations (Combination) : Select item from a number of combinations of r n items, the order is not considered. Combination count 
rules: .
Arrangement (Permutation) : Select item from a number of combinations of r n items in consideration of the order. Counting arrangement 
rules: .
Bayes Theorem (Bayes's Theorem) : probability of correcting the latter method to obtain new information. Priori probability ---> new information ---> Bayes Theorem ---> posterior probability. DETAILED See: Bayes theorem, (Bayes Theorem is apos) .
Discrete probability distribution (Discrete Probability Distribution's) : Bernoulli distribution, binomial distribution, Poisson distribution, geometric distribution, hypergeometric distribution, a number of distribution
The continuous probability distribution (Continous Probability Distribution's) : exponential, normal, uniform
Sampling probability distribution (Probability Sampling Distribution's) : t distribution, chi-square distribution, F distribution
Common types of probability distribution (a) (Probability Distribution I)
Common types of probability distribution (b) (Probability Distribution II)
Probability mass function (Probability Mass Function, PMF) : discrete random variable on the probability of each specific value.
Probability density function (Probability Density Function, PDF) : the probability of continuous random variable values within a certain range, a probability value is not given directly to the area under the curve is a probability, by integrating the need to seek this interval.
(Note: the area under the curve is a single point 0, the probability of continuous random variable takes a particular value is 0.)
CDF (Cumulative Distribution's Function, the CDF) : For the discrete distribution, CDF can be defined to give, while the function of the shape should be stepped; continuous function, are all less than or equal the value of a probability of occurrence and, i.e., F ( a) = P (x <= a), can be obtained by the integral of the probability density function.