Distributions

See also: Distributions in ModelRisk, Vose Select Distribution, Distributions by category

Distributions by category

Continuous Univariate Distributions
- Beta
- Beta4
- Bradford
- Burr
- Cauchy
- Chi
- Chi-Squared
- Ascending Cumulative
- Descending Cumulative
- Dagum
- Error Function
- Erlang
- Error
- Exponential
- Extreme Value Max
- Extreme Value Min
- F
- Fatigue Life(time)
- Gamma
- Generalised Logistic
- Generalized Extreme Value
- Generalized Trapezoid Uniform
- Histogram
- Hyperbolic-Secant
- Inverse Gaussian
- JohnsonB
- JohnsonU
-
KernelCU distribution
- Kumaraswamy
- Kumaraswamy4
- Laplace
- Levy
-
Lifetime2
- Lifetime3

- LifetimeExp
- LogGamma
- Logistic
- LogLaplace
- LogLogistic
- LogLogisticAlt
- Lognormal
- LognormalAlt
- LognormalB
- LognormalE
- LogTriangle
-
LogUniform
- Maxwell
- Modified PERT
-
NCChiSq
- NCF

- Normal
- NormalAlt
-
NormalMix
- Ogive
- Pareto
- Shifted Pareto
- Pearson5
- Pearson6
- PERT
-
PERTAlt
- Rayleigh
- Reciprocal
- Relative
-
Skew Normal
-
Slash
-
Split Triangle
- Student, or t-
- Student3
- Triangle
-
TriangleAlt
- Uniform
- Weibull
-
WeibullAlt
- Weibull3

Discrete Univariate Distributions
- Bernoulli
- BetaBinomial
- BetaGeometric
- BetaNegBin
- Binomial
- Burnt Finger Poisson
- Delaporte
- Discrete
- Discrete Uniform
- Geometric
- Hypergeometric
- HypergeoD
- HypergeoM
- Inverse Hypergeometric
- Logarithmic
- Negative Binomial
- Poisson
- Poisson Uniform
- Polya
- Skellam
- StepUniform

Multivariate Distributions
-Dirichlet
-Multinomial
-Multivariate Hypergeometric
-Multivariate Inverse Hypergeometric distribution type1
-Multivariate Inverse Hypergeometric distribution type2
-Multivariate Normal
-Negative Multinomial distribution type 1
-Negative Multinomial distribution type 2

Zero-modified counting distributions

Zero-inflated (zeroes added)
- ZI Beta-Binomial equations
- ZI BetaGeometric equations
- ZI BetaNegBin equations
- ZI binomial distribution
- ZI Delaporte equations
- ZI geometric equations
- ZI Hypergeometric equations
- ZI inverse Hypergeometric equations
- ZI Negative Binomial equations
- ZI Poisson equations
- ZI Polya equations
- ZI Skellam

Zero-truncated (zeroes removed)
- ZT beta-binomial equations
- ZT BetaGeometric equations
- ZT betaNegBin equations
- ZT binomial distribution
- ZT Delaporte equations
- ZT Geometric equations
- ZT Hypergeometric equations
- ZT inverse Hypergeometric equations
- ZT Negative Binomial equations
- ZT Poisson equations


Distributions are used in risk analysis to model three conceptually different things
 

  1. The variability of individuals in a population (frequency distribution).

  2. The value of a random variable (probability distribution).

  3. The uncertainty we have about a fixed, but imprecisely known, parameter in nature.


Organization of this section
 

  
Click on an image
to read the explanation associated with it.

 

This section is a resource to allow you to look up specific distributions by name, to see how they are used, learn how to generate their values when they are not directly available in ModelRisk, and review the most useful equations associated with each distribution. We have also described the inter-relationships between various distributions and provided links to related topics.

The distributions are split into two categories: discrete and continuous. We explain how what you need to think about when selecting a distribution to go in your model. We also provide a section that describes how you can approximate one distribution with another, or use recursive formulae, so that you can avoid possible parameter restrictions..

Finally, if none of the distributions we describe suit your purpose, you can always create your own! We show you a variety of ways to do that.


Useful formulae and functions
 

When reviewing the formulae associated with each distribution you will frequently come across a number of unusual (though not very complicated!) mathematical functions that it is worth explaining here:

Binomial coefficient

The binomial coefficient is defined as follows:

where:  n! = 1 * 2 * 3 * .. * (n-1) * n

In Excel use =COMBIN(n,r). The Excel function FACT(n) also returns n!


The Gamma function
 

Γ(n) is the gamma function and has the following properties:

Γ(n+1) = nΓ(n) = n!       where n is an integer

Γ(0.5) = √π

Γ(0) = 1

So, for example, Γ (1.5) = 1/2 * Γ (0.5) = 1/2 √π . Excel offers the function GAMMALN( ) which returns the natural log of the Gamma function, so to get Γ(n) you write =EXP(GAMMALN(n)).


Other functions
 

tan-1(x)

The arc tan of x in radians. =ATAN(x) in Excel

|x|

The absolute value of x, i.e. |-x| = x, |x| = x. Use =ABS(x) in Excel

csc(x)

The cosecant of x, = 1/sin(x). Use =1/SIN(x) in Excel

The nearest integer at or below x. Use =ROUNDDOWN(x,0) in Excel

The nearest integer at or above x. Use =ROUNDUP(x,0) in Excel

ln(x)

The natural log of x, so that x = exp(ln(x)). Use =LN(x) in Excel

exp(x), ex

The natural exponent of x, = 2.718281828..x. Use =EXP(x) in Excel

 



Read on: How to read probability distribution equations
















 

 

ModelRisk

Monte Carlo simulation in Excel. Learn more

Tamara

Adding risk and uncertainty to your project schedule. Learn more

Navigation

FREE MONTE CARLO SIMULATION SOFTWARE

For Microsoft Excel

Download your free copy of ModelRisk Basic today. Professional quality risk modeling software and no catches

Download ModelRisk Basic now

FREE PROJECT RISK SOFTWARE

For Primavera & Microsoft Project

Download your free copy of Tamara Basic today. Professional quality project risk software and no catches.

Download Tamara Basic now
-->