Probability distributions

Analytica User GuideProbability DistributionsProbability distributions

The built-in Distribution library (available from the Definition menu) offers a wide range of distributions for discrete and continuous variables. (See Is the quantity discrete or continuous?] and Glossary for an explanation of this distinction.) Some are standard or parametric distributions with just a few parameters, such as Normal and Uniform, which are continuous, and Bernoulli and Binomial, which are discrete. Others are custom distributions, such as CumDist, which lets you specify an array of points on a cumulative probability distribution, and Probtable, which lets you edit a table of probabilities for a discrete variable conditional on other discrete variables.

There are a variety of ways to create arrays of uncertain quantities, or multivariate distributions. You may set parameters to array values, specify an index to the optional Over parameter, or use functions from the Multivariate library.

You can find more information about various probability functions in the Analytica wiki:

Parametric Discrete

• Bernoulli()
• Binomial()
• Poisson()
• Geometric()
• Hypergeometric()
• NegativeBinomial()
• Uniform()
• Wilcoxon()

Custom Discrete

• Probtable()
• Determtable()
• Chancedist()

Special Probabilistic

• Certain()
• Shuffle()
• Truncate()
• Random()

Parametric Continuous

• Uniform()
• Triangular()
• Normal()
• Lognormal()
• Beta()
• Exponential()
• Gamma()
• Gamma()
• StudentT()
• Weibull()
• ChiSquared()

Custom Continuous

• Cumdist()
• Probdist()

Multivariate

• Normal_correl()
• Correlate_With()
• Dist_reshape()
• Correlate_Dists()
• Gaussian()
• MultiNormal()
• BiNormal()
• Dirichlet()
• Multinomial()
• UniformSpherical()
• MultiUniform()
• Normal_serial_correl()
• Dist_serial_correl()
• Normal_additive_gro()
• Dist_additive_growth()
• Normal_compound_gro()
• Dist_compound_growth()

See Also

Probability DistributionsProbability distributionsParametric discrete distributions