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)

"Monte Carlo is able to discover practical solutions to otherwise intractable problems because the most efficient search of an unmapped territory takes the form of a random walk. Today’s search engines, long descended from their ENIAC-era ancestors, still bear the imprint of their Monte Carlo origins: random search paths being accounted for, statistically, to accumulate increasingly accurate results. The genius of Monte Carlo - and its search-engine descendants - lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths." (George B Dyson, "Turing's Cathedral: The Origins of the Digital Universe", 2012)

"The genius of Monte Carlo - and its search-engine descendants - lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths." (George B Dyson, "Turing's Cathedral: The Origins of the Digital Universe", 2012)

"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)