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A model that seeks to find the best solution from a set of alternatives, often by maximizing or minimizing an objective function. Linear programming used in resource allocation is an example.

A model that does not account for time and is used to assess a system at a particular instant. An example is the calculation of stress on a beam in civil engineering.

A probabilistic model for which a graph expresses the conditional dependency structure between random variables. Bayesian networks in machine learning are a typical application.

A model where the variables can take on an infinite number of values within a range, represented by integrals and differential equations. Fluid dynamics often relies on continuous models.

A representation of a system that is informed by statistical methods, where the model parameters are estimated from data. An application is the use of Markov Chain models in predicting weather patterns.

A model where the relationship between variables is not proportional and may involve exponents or other nonlinear functions. For example, modeling the spread of epidemics often uses nonlinear differential equations.

A computational model for simulating the actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole. An example is simulating traffic flow in an urban environment.

A model where outcomes are precisely determined through known relationships without any randomness. For example, predicting planetary motion using Newton's laws of motion.

A model that is defined for distinct and separate values, often using summation or recursive relations. An example is the Fibonacci sequence used in computer algorithms.

A model used to imitate the operation of a real-world process or system over time. For example, Monte Carlo simulations are used in finance to model the probability of different outcomes for an uncertain event.

A mathematical model that has a closed-form solution, meaning that it can be solved analytically. Solving traffic flow at an intersection using the kinematic wave model is an example.

This type of model represents processes that change over time, using differential or difference equations. A common application is in population dynamics, such as the predator-prey model described by the Lotka-Volterra equations.

A model where the relationship between variables is proportional and can be represented by linear equations. For instance, linear regression is used to predict sales based on advertising spend.

In epidemiology, this model divides the population into compartments with assumptions about the rates of change between compartments. The SIR model for the spread of infectious diseases is a well-known application.