Continuous distributions | Vose Software

Continuous distributions

Continuous distributions can take any value within a defined range. This range may be infinite (e.g. for the Normal distribution) in which case we speak of an unbounded distribution or finite (e.g. the Uniform distribution) in which case we speak of a bounded distribution.

List of continuous distributions

The table below gives an overview of various continuous distributions commonly used in risk analysis modeling, so that you can most easily focus on which ones might be most appropriate for your modeling needs. Follow the links for an in-depth explanation of each. We have used the most common name for each distribution.


Example use


Models uncertainty or variation of a probability, fraction or prevalence.


Description of population variation.


Models the points of impact of a fixed straight line of particles emitted from a point source. Ratio of two Normal distributions.

Chi Squared

The sum of unit Normal distributions squared. Used widely in classical statistics where sample measures can be transformed to be approximately a sum of unit Normal distributions squared too.

Cumulative ascending

Used to create an empirically-based distribution. Useful in creating a non-parametric fitted to data.

Cumulative descending

Another form of the cumulative distribution. Uses the probability of being greater than or equal to their corresponding x-values.


Used to describe uncertainty about the probabilities of a Multinomial distribution: a multi-dimensional version of a Beta distribution.


Another format for the Normal distribution with a zero mean.


A special case of the Gamma distribution where the first parameter is discrete.


Models the time until an event occurs in a Poisson process.

Extreme value

Models the distribution of the extreme values that a variable can take.


Used in statistics to compare the variance between two (assumed Normally distributed) populations.


Models the distribution of the extreme values that a variable can take.


Models the time until a number of events occurs in a Poisson process.


Used to create an empirically-based distribution from relative frequency data.

Generalized Extreme Value

A distribution that incorporates the Fréchet, Weibull and Gumbel extreme value families of distributions.


Useful for replicating the distribution shape of a set of data.

Inverse Gaussian

Models the time to cover a distance in Brownian motion.


Fitting a bounded distribution to known moments.


Fitting an unbounded distribution to known moments.


A kernel estimated distribution based on a set of data, assuming the variable is continuous (C) and unbounded (U).


A symmetric distribution, useful for having longer tails than a Normal distribution.


The lifetime of a device with a linear hazard rate.


The lifetime of a device with a quadratic hazard rate.


The lifetime of a device with an exponentially increasing hazard rate.


An asymmetric distribution which offers a greater variety of shapes.


Popular in demographic and economic modeling, mostly as a growth equation. Similar to a Normal distribution, but more peaked.


The log of the logistic distribution, so if X is loglogistically distributed, log X is logistically distributed.

Lognormal (format 1)

Useful for modeling naturally occurring variables that are the product of a number of other naturally occurring variables. If log X is Normally distributed, then X is lognormally distributed.

Lognormal (format 2)

An alternative way of defining a Lognormally distributed variable, using the mean and standard deviation of the corresponding Normal.


Used when estimating a variable with a range covering orders of magnitude.


Used when estimating a variable with a range covering orders of magnitude.


A distribution often used in physics and engineering.


Models variations of naturally occurring variables. Also an approximate distribution to many other distributions in certain circumstances.


Used for modeling expert estimates of some continuous quantity with equal tails, like time or cost to complete a task.


Used where a Normal distribution gives insufficient peakedness (kurtosis).

Pareto (1st kind)

Used to model any variable that has a minimum, and also its most likely, value and for which the probability density decreases geometrically towards zero. Often used because it has a very long right tail.

Pareto (2nd kind)

Is a shifted Pareto distribution.

Pearson V

A member of the Pearson system of distributions, and little used.

Pearson VI

Another member of the Pearson system of distributions.


A smoothed, triangular-like distribution, based on the Beta distribution.

Modified PERT

A more controllable version of the PERT distribution.


A distribution based on the cumulative distribution of a data set.


A special case of the Weibull distribution. Models distance to nearest neighbour where they are Poisson distributed in space.

Skew Normal

Used where a Normal distribution gives insufficient lopsidedness (skewness).


Developed as a deviation to the Normal distribution to allow for fatter tails.


Used in statistical estimation.


Used as a very approximate model where there are very little or no available data.

Triangle (various)

Used as a rough modeling tool where the range and the most likely value within the range can be estimated.


Used to model the time until occurrence of an event where the momentary probability of occurrence changes over time.

Weibull3 The lifetime of a device where there is no possibility of failure for some limited time.

A continuous distribution is used to represent a variable that can take any value within a defined range (domain). For example, the height of an adult English male picked at random will have a continuous distribution because the height of a person is essentially infinitely divisible. We could measure his height to the nearest centimetre, millimetre, tenth of a millimetre, etc. The scale can be repeatedly divided up generating more and more possible values.

Properties like time, mass and distance, that are infinitely divisible, are modelled using continuous distributions. In practice, we also use continuous distributions to model variables that are, in truth, discrete but where the gap between allowable values is insignificant: for example, project cost (which is discrete with steps of one penny, one cent, etc.), exchange rate (which is only quoted to a few significant figures), number of employees in a large organization, etc.

The vertical scale of a relative frequency plot of an input continuous probability distribution is the probability density. It does not represent the actual probability of the corresponding x-axis value since that probability is zero. Instead, it represents the probability per x-axis unit of generating a value within a very small range around the x-axis value.

In a continuous relative frequency distribution, the area under the curve must equal one. This means that the vertical scale must change according to the units used for the horizontal scale. For example, the figure below shows a theoretical distribution of the cost of a project using Normal(Ј4 200 000, Ј350 000).


Since this is a continuous distribution, the cost of the project being precisely Ј4M is zero. The vertical scale reads a value of 9.7x10-7 (about one in a million). The x-axis units are Ј1, so this y-axis reading means that there is a one in a million chance that the project cost will be Ј4M plus or minus 50p (a range of Ј1). By comparison, the figure below shows the same distribution but using million pounds as the scale i.e. Normal(4.2, 0.35). The y-axis value at x = Ј4M is 0.97, one million times the above value.

This does not however mean that there is a 97% chance of being between Ј3.5M and Ј4.5M, because the probability density varies very considerably over that range. The logic used in interpreting the 9. 7x10-7 value for the first figure is an approximation that is valid there because the probability density is essentially constant over that range (Ј4M +/- 50p).

The links below discuss different ways of categorizing distributions that may help in your selection of the most appropriate distribution to use:

Bounded and unbounded distributions

Parametric and non-parametric distributions

Discrete distributions