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A discrete-event simulation (DES) models the operation of a system as a sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. [ 1 ] Between consecutive events, no change in the system is assumed to occur; thus the simulation time can directly jump to the occurrence time of the ...
In the field of simulation, a discrete rate simulation models the behavior of mixed discrete and continuous systems. This methodology is used to simulate linear continuous systems, hybrid continuous and discrete-event systems, and any other system that involves the rate-based movement or flow of material from one location to another. [1]
Continuous dynamic systems can only be captured by a continuous simulation model, while discrete dynamic systems can be captured either in a more abstract manner by a continuous simulation model (like the Lotka-Volterra equations for modeling a predator-prey eco-system) or in a more realistic manner by a discrete event simulation model (in a ...
Data collection systems are an end-product of software development. Identifying and categorizing software or a software sub-system as having aspects of, or as actually being a "Data collection system" is very important. This categorization allows encyclopedic knowledge to be gathered and applied in the design and implementation of future systems.
A mixed random variable is a random variable whose cumulative distribution function is neither discrete nor everywhere-continuous. [10] It can be realized as a mixture of a discrete random variable and a continuous random variable; in which case the CDF will be the weighted average of the CDFs of the component variables. [10]
Modeling values that vary over continuous time, called "behaviors" and later "signals". Modeling "events" which have occurrences at discrete points in time. The system can be changed in response to events, generally termed "switching." The separation of evaluation details such as sampling rate from the reactive model.
A continuous-time Markov chain is like a discrete-time Markov chain, but it moves states continuously through time rather than as discrete time steps. Other stochastic processes can satisfy the Markov property, the property that past behavior does not affect the process, only the present state.
Furthermore, it covers distributions that are neither discrete nor continuous nor mixtures of the two. An example of such distributions could be a mix of discrete and continuous distributions—for example, a random variable that is 0 with probability 1/2, and takes a random value from a normal distribution with probability 1/2.