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Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making. [ 1 ][ 2 ] In the computer application of modeling and simulation a computer is used ...
A simulation is an imitative representation of a process or system that could exist in the real world. [ 1 ][ 2 ][ 3 ] In this broad sense, simulation can often be used interchangeably with model. [ 2 ] Sometimes a clear distinction between the two terms is made, in which simulations require the use of models; the model represents the key ...
Financial modeling is the task of building an abstract representation (a model) of a real world financial situation. [1] This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business, project, or any other investment. Typically, then, financial modeling is understood ...
Essentially, the Monte Carlo method solves a problem by directly simulating the underlying (physical) process and then calculating the (average) result of the process. [ 1 ] This very general approach is valid in areas such as physics, chemistry, computer science etc. In finance, the Monte Carlo method is used to simulate the various sources of ...
Randomness test. A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for ...
Simulation-based optimization. Simulation-based optimization (also known as simply simulation optimization) integrates optimization techniques into simulation modeling and analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate. Usually, the underlying simulation model is ...
Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict steady state condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation.
First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables.
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