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description=A random process models the progression of a system over time, where the evolution is random rather than deterministic. The key point is that observations that are close in time are dependent, and this can be used to model, simulate, and predict the behavior of the process. Random processes are used in a variety of fields including economics, finance, engineering, physics, and biology. Building on its strong capabilities for distributions, the Wolfram Language provides cohesive and comprehensive random process support. Using a symbolic representation of a process makes it easy to simulate its behavior, estimate parameters from data, and compute state probabilities at different times. There is additional functionality for special classes of random processes such as Markov chains, queues, time series, and stochastic differential equations.;

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queueingnetworkprocess queueingprocess randomfunction randomwalkprocess renewalprocess sarimaprocess slicedistribution stationarydistribution stratonovichprocess temporaldata transformedprocess weakstationarity whitenoiseprocess wienerprocess related guides probability statistics finance signal processing time series processing event series processing functions absolutecorrelationfunction armaprocess continuousmarkovprocess correlationfunction covariancefunction discretemarkovprocess estimatedprocess findprocessparameters itoprocess mean poissonprocess probability processparameterassumptions processparameterq queueingnetworkprocess queueingprocess randomfunction randomwalkprocess renewalprocess sarimaprocess slicedistribution stationarydistribution stratonovichprocess temporaldata transformedprocess weakstationarity whitenoiseprocess wienerprocess related guides probability statistics finance signal processing time series processing event series processing guide functions absolutecorrelationfunction armaprocess continuousmarkovprocess correlationfunction covariancefunction discretemarkovprocess estimatedprocess findprocessparameters itoprocess mean poissonprocess probability processparameterassumptions processparameterq queueingnetworkprocess queueingprocess randomfunction randomwalkprocess renewalprocess sarimaprocess slicedistribution stationarydistribution stratonovichprocess temporaldata transformedprocess weakstationarity whitenoiseprocess wienerprocess related guides probability statistics finance signal processing time series processing event series processing functions absolutecorrelationfunction armaprocess continuousmarkovprocess correlationfunction covariancefunction discretemarkovprocess estimatedprocess findprocessparameters itoprocess mean poissonprocess probability processparameterassumptions processparameterq queueingnetworkprocess queueingprocess randomfunction randomwalkprocess renewalprocess sarimaprocess slicedistribution stationarydistribution stratonovichprocess temporaldata transformedprocess weakstationarity whitenoiseprocess wienerprocess related guides probability statistics finance signal processing time series processing event series processing random processes a random process models the progression of a system over time where the evolution is random rather than deterministic the key point is that observations that are close in time are dependent and this can be used to model simulate and predict the behavior of the process random processes are used in a variety of fields including economics finance engineering physics and biology building on its strong capabilities for distributions the wolfram language provides cohesive and comprehensive random process support using a symbolic representation of a process makes it easy to simulate its behavior estimate parameters from data and compute state probabilities at different times there is additional functionality for special classes of random processes such as markov chains queues time series and stochastic differential equations simulation estimation randomfunction simulate a random process temporaldata represent one or several time series data estimatedprocess findprocessparameters estimate process parameters from data process distributions probability compute probabilities of predicates of process state at different times slicedistribution process state distribution at time stationarydistribution process state distribution at time processparameterassumptions processparameterq process moments mean mean function for a process covariancefunction covariance function for a process weakstationarity conditions for a process to be weakly stationary correlationfunction absolutecorrelationfunction parametric processes randomwalkprocess poissonprocess wienerprocess derived processes whitenoiseprocess transformedprocess renewalprocess markov processes discretemarkovprocess continuousmarkovprocess queueing processes queueingprocess 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