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MATHEMATICA GUIDE
Derived Statistical Distributions
Derived distributions are modifications to existing distributions. There is a variety of ways in which you can arrive at modified distributions, including functions of random variables, weighted mixtures of distributions, truncated or censored distributions, marginals from higher-dimensional distributions, or joining marginals to a dependency kernel, as in copulas. Derived distributions behave just like any other distribution in Mathematica. You can compute several dozen properties, including distribution functions or probabilities of events, or generate random variates from them.
Featured Examples |
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Apply Censoring to a Distribution
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Compare Nonparametric and Parametric Reliability Models
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Compute a Complex Probability
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Decompose Mixture Models of Earthquake Magnitudes
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Model Claim Payments for Insurance
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Order Statistics Distribution for General Multivariate Distribution
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Perform Affine Transformations on a Normal Distribution
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Simulate a Derived Distribution
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Splice the Body of a Distribution with New Tails
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Truncate a Distribution
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Use Component Mixtures to Model Multimodal Data
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Use Different Copula Kernels
