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Glossary

esseff edited this page Oct 25, 2021 · 1 revision

Home > Glossary

The following table contains a list of some terms used in this wiki along with a brief explanation of each.

Term Explanation
API Short for Application Program Interface, which is a well-defined set of methods by which code in one application can make use of functionality in another.
case‑based model A microsimulation model which sequentially simulates a set of independent cases, where each case consists of one or more entities. There is no coordination of time between cases.
OpenM++ A platform for developing, running and deploying microsimulation models
Modgen A platform for developing and running microsimulation models on Windows. Predecessor of OpenM++
time‑based model A microsimulation model in which time advances in a cooridnated way for all entities in a simulation.
replicate A duplicate of a simulation, with some input parameters varied to represent uncertainty. For example, varying the input parameter containing the master seed for random number generation in a set of replicates causes variation in model results across those replicates, allowing assessment of the effect of Monte Carlo Error on model results.
MCE See Monte Carlo Error
Monte Carlo Error The uncertainty in aggregate results of a model due to the use of a Random Number Generator to sample distributions. Monte Carlo Error can be reduced by increasing population size or by averaging a larger number of replicate simulations.
RNG See Random Number Generator
Random Number Generator A deterministic algorithm which generates a sequence of apparently uncorrelated values which can be used to draw a sample from a statistical distribution
entity An object which participates in a microsimulation model, such as a Person. An entity can have attributes and can evolve through time as the simulation progresses.
attribute A characteristic of an entity, such as age.
identity attribute An attribute which is identical to a mathematical expression involving other attributes

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Getting Started

Model development in OpenM++

Using OpenM++

Model Development Topics

OpenM++ web-service: API and cloud setup

Using OpenM++ from Python and R

Docker

OpenM++ Development

OpenM++ Design, Roadmap and Status

OpenM++ web-service API

GET Model Metadata

GET Model Extras

GET Model Run results metadata

GET Model Workset metadata: set of input parameters

Read Parameters, Output Tables or Microdata values

GET Parameters, Output Tables or Microdata values

GET Parameters, Output Tables or Microdata as CSV

GET Modeling Task metadata and task run history

Update Model Profile: set of key-value options

Update Model Workset: set of input parameters

Update Model Runs

Update Modeling Tasks

Run Models: run models and monitor progress

Download model, model run results or input parameters

Upload model runs or worksets (input scenarios)

Download and upload user files

User: manage user settings

Model run jobs and service state

Administrative: manage web-service state

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