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Helpers for expert elicitation into distributions.

Project description

Elicited

Helper tools to construct probability distributions built from expert elicited data for use in monte carlo simulations.

Usage

pip install elicited
import elicited

elicited is just a helper tool when using numpy and scipy, so you'll need these in your code.

import numpy as np
import scipy

Lognormal

See Occurance and Applications for examples of lognormal distributions in nature.

Expert: Most customers hold around $20K (val_mod) but I could imagine a customer with $2.5M (val_max)

logN_mean, logN_stdv = d.elicitLogNormal(val_mod, val_max)

Pareto

The 80/20 rule. See Occurance and Applications

Expert: The legal costs of an incident could be devastating. Typically costs are almost zero (val_min) but a black swan could be $100M (val_max).

b = elicitPareto(val_min, val_max)
p = pareto(b, loc=val_min-1., scale=1.))

PERT

See PERT Distribution

Expert: Our customers have anywhere from $500-$6000 (val_min / val_max), but it's most typically around $4500 (val_mod)

PERT_a, PERT_b = elicitPERT(val_min, val_mod, val_max)
pert = beta(PERT_a, PERT_b, loc=val_min, scale=val_max-val_min)

Zipf's

See Applications

Expert: If we get sued, there will only be a few litigants (nMin). Very rarely it could be 30 or more litigants (nMax), maybe once every thousand cases (pMax) it would be more.

Zs = elicitZipf(nMin, nMax, pMax, report=True)

pd = zipf(Zs, nMin-1)

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