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

Related tags

Data Analysiselicited
Overview

Elicited

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

Credit to Brett Hoover, packaging by @magoo

Usage

pip install elicited
import elicited as e

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

import numpy as np
from scipy.stats import poisson, zipf, beta, pareto, lognorm

Lognormal

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

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

mode = 20000
max = 2500000

mean, stdv = e.elicitLogNormal(mode, max)
asset_values = lognorm(s=stdv, scale=np.exp(mean))
asset_values.rvs(100)

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 = e.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 = e.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.

nMin = 1
nMax = 30
pMax = 1/1000

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

litigants = zipf(Zs, nMin-1)

litigants.rvs(100)

Reference: Other Useful Elicitations

Listed as a courtesy, these distributions are simple enough to elicit data into directly without a helper function.

Uniform

A "zero knowledge" distribution where all values within the range have equal probability of appearing. Similar to random.randint(a, b)

Expert: The crowd will be between 50 (min) and 500 (max) due to fire code restrictions and the existing residents in the building.

from scipy.stats import uniform

min = 50
max = 500

range = max - min

crowd_size = uniform(min, range)
crowd_size.rvs(100)

Poisson

Expert: About 3000 Customers (average) add a credit card to their account every quarter.

from scipy.stats import poisson
average = 3000
upsells = poisson(average)
upsells.rvs(100)
Owner
Ryan McGeehan
Founder / Advisor @ HackerOne Former Director of Security @ Coinbase Former Director of Security @ Facebook
Ryan McGeehan
Lale is a Python library for semi-automated data science.

Lale is a Python library for semi-automated data science. Lale makes it easy to automatically select algorithms and tune hyperparameters of pipelines that are compatible with scikit-learn, in a type-

International Business Machines 293 Dec 29, 2022
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

pgmpy 2.2k Dec 25, 2022
A powerful data analysis package based on mathematical step functions. Strongly aligned with pandas.

The leading use-case for the staircase package is for the creation and analysis of step functions. Pretty exciting huh. But don't hit the close button

48 Dec 21, 2022
A distributed block-based data storage and compute engine

Nebula is an extremely-fast end-to-end interactive big data analytics solution. Nebula is designed as a high-performance columnar data storage and tabular OLAP engine.

Columns AI 131 Dec 26, 2022
PyPSA: Python for Power System Analysis

1 Python for Power System Analysis Contents 1 Python for Power System Analysis 1.1 About 1.2 Documentation 1.3 Functionality 1.4 Example scripts as Ju

758 Dec 30, 2022
Larch: Applications and Python Library for Data Analysis of X-ray Absorption Spectroscopy (XAS, XANES, XAFS, EXAFS), X-ray Fluorescence (XRF) Spectroscopy and Imaging

Larch: Data Analysis Tools for X-ray Spectroscopy and More Documentation: http://xraypy.github.io/xraylarch Code: http://github.com/xraypy/xraylarch L

xraypy 95 Dec 13, 2022
track your GitHub statistics

GitHub-Stalker track your github statistics 👀 features find new followers or unfollowers find who got a star on your project or remove stars find who

Bahadır Araz 34 Nov 18, 2022
2019 Data Science Bowl

Kaggle-2019-Data-Science-Bowl-Solution - Here i present my solution to kaggle 2019 data science bowl and how i improved it to win a silver medal in that competition.

Deepak Nandwani 1 Jan 01, 2022
A library to create multi-page Streamlit applications with ease.

A library to create multi-page Streamlit applications with ease.

Jackson Storm 107 Jan 04, 2023
Developed for analyzing the covariance for OrcVIO

about This repo is developed for analyzing the covariance for OrcVIO environment setup platform ubuntu 18.04 using conda conda env create --file envir

Sean 1 Dec 08, 2021
Weather Image Recognition - Python weather application using series of data

Weather Image Recognition - Python weather application using series of data

Kushal Shingote 1 Feb 04, 2022
Clean and reusable data-sciency notebooks.

KPACUBO KPACUBO is a set Jupyter notebooks focused on the best practices in both software development and data science, namely, code reuse, explicit d

Matvey Morozov 1 Jan 28, 2022
Hydrogen (or other pure gas phase species) depressurization calculations

HydDown Hydrogen (or other pure gas phase species) depressurization calculations This code is published under an MIT license. Install as simple as: pi

Anders Andreasen 13 Nov 26, 2022
Very useful and necessary functions that simplify working with data

Additional-function-for-pandas Very useful and necessary functions that simplify working with data random_fill_nan(module_name, nan) - Replaces all sp

Alexander Goldian 2 Dec 02, 2021
Demonstrate a Dataflow pipeline that saves data from an API into BigQuery table

Overview dataflow-mvp provides a basic example pipeline that pulls data from an API and writes it to a BigQuery table using GCP's Dataflow (i.e., Apac

Chris Carbonell 1 Dec 03, 2021
MDAnalysis is a Python library to analyze molecular dynamics simulations.

MDAnalysis Repository README [*] MDAnalysis is a Python library for the analysis of computer simulations of many-body systems at the molecular scale,

MDAnalysis 933 Dec 28, 2022
Tools for working with MARC data in Catalogue Bridge.

catbridge_tools Tools for working with MARC data in Catalogue Bridge. Borrows heavily from PyMarc

1 Nov 11, 2021
Python Implementation of Scalable In-Memory Updatable Bitmap Indexing

PyUpBit CS490 Large Scale Data Analytics — Implementation of Updatable Compressed Bitmap Indexing Paper Table of Contents About The Project Usage Cont

Hyeong Kyun (Daniel) Park 1 Jun 28, 2022
Automatic earthquake catalog building workflow: EQTransformer + Siamese EQTransformer + PickNet + REAL + HypoInverse

Automatic regional-scale earthquake catalog building workflow: EQTransformer + Siamese EQTransforme

Xiao Zhuowei 9 Nov 27, 2022
Building house price data pipelines with Apache Beam and Spark on GCP

This project contains the process from building a web crawler to extract the raw data of house price to create ETL pipelines using Google Could Platform services.

1 Nov 22, 2021