### Bernoulli process

An experiment whose dichotomous outcomes are random (coin toss, rolling a die, polls).

### Beta Distribution

models events that are constrained to take place within an interval defined by a minimum and maximum value.The Beta probability density function is given by
$\frac {x^{\alpha-1}(1-x)^{\beta-1}} {B(\alpha,\beta)}$
where

• α is a positive shape parameter
• β is a positive shape parameter

The Beta distribution is used in a range of disciplines including rule of succession, Bayesian statistics, and task duration modeling. Examples of events that may be modeled by Beta distribution include:

• The time it takes to complete a task
• The proportion of defective items in a shipment

### Geometric distribution

The probability distribution of the number of Bernoulli trials needed to get one success.

### Hyper Geometric

Discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement.

### Poisson distribution

A discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event, that is, it predicts the degree of spread around a known average rate of occurrence.
$\frac {\lambda^k e^{-\lambda}} {k!}$
where

• e is the base of the natural logarithm (e = 2.71828…)
• k! is the factorial of k
• λ is a positive real number, equal to the expected number of occurrences during the given interval. As a function of k, this is the probability mass function. The parameter λ is not only the mean number of occurrences , but also its variance.

### Gamma distribution

Gamma distribution is a distribution that arises naturally in processes for which the waiting times between events are relevant. It can be thought of as a waiting time between Poisson distributed events.
The waiting time until the hth Poisson event with a rate of change λ is $P(x)=\lambda(\lambda x)^{h-1}$
The gamma distribution can be used a range of disciplines including queuing models, climatology, and financial services. Examples of events that may be modeled by gamma distribution include:

• The amount of rainfall accumulated in a reservoir
• The size of loan defaults or aggregate insurance claims
• The flow of items through manufacturing and distribution processes
• The load on web servers
• The many and varied forms of telecom exchange

The gamma distribution is also used to model errors in a multi-level Poisson regression model because the combination of a Poisson distribution and a gamma distribution is a negative binomial distribution.

### Exponential Distribution

A special case of the gamma distribution. The gamma distribution is the waiting time for more than one event, the exponential distribution describes the time between a single Poisson event. The exponential probability density function is given by
$e^{-\lambda x}$
where:

• e is the natural number (e = 2.71828…)
• λ is the mean time between events
• x is a random variable

The exponential distribution occurs naturally when describing the waiting time in a homogeneous Poisson process. It can be used in a range of disciplines including queuing theory, physics, reliability theory, and hydrology. Examples of events that may be modeled by exponential distribution include:

• The time until a radioactive particle decays
• The time between clicks of a Geiger counter
• The time until default on payment to company debt holders
• The distance between mutations on a DNA strand
• The time it takes for a bank teller to serve a customer
• The height of various molecules in a gas at a fixed temperature and pressure in a uniform gravitational field
• The monthly and annual maximum values of daily rainfall and river discharge volumes

### Pareto Distribution

A skewed, heavy-tailed distribution that is sometimes used to model that distribution of incomes. The basis of the distribution is that a high proportion of a population has low income while only a few people have very high incomes. The Pareto probability density function is given by
$\frac {ax^a_m} {x^{a+1}}$
where

• $x_m$ is the minimum possible value of X
• α is a positive parameter which determines the concentration of data towards the mode
• x is a random variable (x > $x_m$)

The Pareto distribution is sometimes expressed more simply as the “80-20 rule”, which describes a range of situations. In customer support, it means that 80% of problems come from 20% of customers. In economics, it means 80% of the wealth is controlled by 20% of the population. Examples of events that may be modeled by Pareto distribution include:

• The sizes of human settlements (few cities, many villages)
• The file size distribution of Internet traffic which uses the TCP protocol (few larger files, many smaller files)
• Hard disk drive error rates
• The values of oil reserves in oil fields (few large fields, many small fields)
• The length distribution in jobs assigned supercomputers (few large ones, many small ones)
• The standardized price returns on individual stocks
• The sizes of sand particles
• The sizes of meteorites
• The number of species per genus
• The areas burned in forest fires
• The severity of large casualty losses for certain businesses, such as general liability, commercial auto, and workers compensation