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SOLUTIONS
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Functions
- BernoulliDistribution
- BetaDistribution
- BinomialDistribution
- BinormalDistribution
- CopulaDistribution
- DirichletDistribution
- DiscreteUniformDistribution
- ErlangDistribution
- ExponentialDistribution
- FrechetDistribution
- GumbelDistribution
- JohnsonDistribution
- LevyDistribution
- LogisticDistribution
- LogNormalDistribution
- MultinomialDistribution
- MultinormalDistribution
- MultivariateHypergeometricDistribution
- MultivariatePoissonDistribution
- MultivariateTDistribution
- NegativeMultinomialDistribution
- NormalDistribution
- ParetoDistribution
- PearsonDistribution
- PoissonDistribution
- StableDistribution
- StudentTDistribution
- TukeyLambdaDistribution
- UniformDistribution
- WakebyDistribution
- WeibullDistribution
- Related Guides
Parametric Statistical Distributions
In almost every area where probability and statistics are used there have been found a few parametric distribution families that are known to be good models. The origins vary from combinatorial arguments, such as in urn models, to transformations of existing distributions, or as different kinds of limit processes. The collection of parametric distributions in Mathematica has been selected in order to provide complete modeling frameworks for a variety of areas. The result is the most extensive collection of parametric distributions ever assembled. From a distribution there are dozens of properties, such as distribution functions, moments, or quantiles, that are directly accessible. Parametric distributions are used as arguments to higher-level functions that compute probabilities, expectations, random variates, or parameter estimates from data. Distributions with undetermined parameters can be used throughout, and later the parameters can be solved for or optimized over, etc.
Featured ExamplesFeatured Examples |
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Analyze Energy Production from a Wind Turbine
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Choose Parametric Tests or Their Nonparametric Counterparts
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Compare Nonparametric and Parametric Reliability Models
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Compute a Complex Probability
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Find Operational Rules for a Data Center
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Fit Word Length Data to Distributions
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Improve the Manufacturing of LCD Displays
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Model Word Lengths by Binomial Distributions
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Perform Affine Transformations on a Normal Distribution
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Perform an Edgeworth Expansion to Approximate a Distribution
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Perform Tests of Location and Scale Simultaneously on Multiple Datasets
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Test for Goodness of Fit to Any Distribution or Dataset
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Use a Logistic Distribution to Simulate Fractional Change
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Use Different Copula Kernels
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Visualize Distribution Functions for a Fitted Multivariate Distribution
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Visualize Optimal Parameter Values
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Visualize the Projected Lifetime of a Component
ReferenceReference
Discrete Univariate Distributions »
BernoulliDistribution ▪ BinomialDistribution ▪ PoissonDistribution ▪ ...
Normal-Related Distributions »
NormalDistribution ▪ StudentTDistribution ▪ LogNormalDistribution ▪ ...
Exponential-Related Distributions »
ExponentialDistribution ▪ ErlangDistribution ▪ LogisticDistribution ▪ ...
Bounded Domain Distributions »
BetaDistribution ▪ BinomialDistribution ▪ UniformDistribution ▪ ...
Extreme Value Distributions »
GumbelDistribution ▪ FrechetDistribution ▪ WeibullDistribution ▪ ...
Heavy Tail Distributions »
ParetoDistribution ▪ StableDistribution ▪ LevyDistribution ▪ ...
Quantile-Based Distributions
TukeyLambdaDistribution ▪ WakebyDistribution
Systems of Distributions
JohnsonDistribution ▪ PearsonDistribution
Multivariate Continuous Distributions
UniformDistribution ▪ BinormalDistribution ▪ MultinormalDistribution ▪ MultivariateTDistribution ▪ DirichletDistribution ▪ CopulaDistribution
Multivariate Discrete Distributions
DiscreteUniformDistribution ▪ MultinomialDistribution ▪ MultivariateHypergeometricDistribution ▪ NegativeMultinomialDistribution ▪ MultivariatePoissonDistribution ▪ CopulaDistribution
