In economics, business, engineering, science and other fields, continuous uncertainties frequently arise that are not easily- or well-characterized by previously-named continuous probability distributions. Frequently, there is data available from measurements, assessments, derivations, simulations or other sources that characterize the range of an uncertainty. But the underlying process that generated this data is either unknown or fails to lend itself to convenient derivation of equations that appropriately characterize the probability density (PDF), cumulative (CDF) or quantile (inverse CDF) distribution functions.
Introduced in 2016, the metalog distributions are a family of continuous probability distributions that directly address this need. Compared to traditional distributions like the normal, lognormal, beta, extreme value, student-t, the Pearson family of distributions, and dozens of others, metalogs offer the following advantages: much greater shape-flexibility to match data from any source (e.g. empirical data, expert-assessed data, or simulated data); choice of boundedness (unbounded, semi-bounded, or bounded); simplicity of equations; ease of fitting to data with ordinary least squares; closed form
quantile function that facilitates simulation; and implementation in spreadsheet software without macros or advanced statistical functions or packages.
Among the most flexible and easy-to-use continuous probability distributions, the metalogs have an increasingly wide range of practical uses:
Converting scientific data from any field into a shape-flexible continuous probability distribution with simple closed-form equations
Visualizing data: instantly seeing a shape-flexible (including multi-modal) PDF that corresponds to your data
Quantifying expert-assessed uncertainties (e.g. 10/50/90 quantiles with optional lower and/or upper bounds) and providing instant visual feedback
Simulating from a shape-flexible closed-form quantile function.
Overall, metalogs are useful for wide range of situations in which CDF data are known, and a simple, flexible, easy-to-use continuous probability distribution is needed to represent that data.
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