Data Science: Monte Carlo Simulation (Definitions)

"A computer-simulation technique that uses sampling from a random number sequence to simulate characteristics or events or outcomes with multiple possible values." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"A simulation in which random events are modeled using pseudo random number generators so that many replications of the random events may be evaluated statistically." (Norman Pendegraft & Mark Rounds, "Dynamic System Simulation for Decision Support", 2008)

"A range of computational algorithms that generates random samples from distributions with known overall properties that is used, for example, to explore potential future behaviours of financial instruments on the basis of historic properties." (Bin Li & Lee Gillam, "Grid Service Level Agreements Using Financial Risk Analysis Techniques", 2010)

"A process which generates hundreds or thousands of probable performance outcomes based on probability distributions for cost and schedule on individual tasks. The outcomes are then used to generate a probability distribution for the project as a whole." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"The technique used by project management applications to estimate the likely range of outcomes from a complex random process by simulating the process a large number of times." (Christopher Carson et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)

"A method for estimating uncertainty in a variable which is a complex function of one or more probability distributions; it uses random numbers to provide an estimate of the distribution and a random number generator to produce random samples from the probabilistic levels." (María C Carnero, "Benchmarking of the Maintenance Service in Health Care Organizations", 2017)

"An analysis technique where a computer model is iterated many times, with the input values chosen at random for each iteration driven by the input data, including probability distributions and probabilistic branches. Outputs are generated to represent the range of possible outcomes for the project." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"A computerized simulation technique which is usually used for analyzing the behaviour of a system or a process involving uncertainties." (Henry Xu & Renae Agrey, "Major Techniques and Current Developments of Supply Chain Process Modelling", 2019)

"'What if' analysis of the future project scenarios, provided a mathematical/ logical model of the project implemented on a computer." (Franco Caron, "Project Control Using a Bayesian Approach", 2019)