What Statistical glossary random process is
A random process, in statistics, is a process of generating a sequence of random variables or values in a probabilistic manner. It is a mathematical model that describes a process that evolves over time according to certain rules.
Steps for “Statistical Glossary Random Process”:
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Define the scope of the process: Identify the process’s domain (i.e. the set of values that it can take on) and the rules that govern its behavior.
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Generate a random sequence of values: This can be done by using a random number generator or by sampling from a probability distribution.
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Observe the process over time: This involves collecting data that describes the process’s behavior over a period of time.
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Analyze the data: This involves using statistical techniques to identify patterns in the data and to make predictions about the future behavior of the process.
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Compare the results to the original model: This involves evaluating the model’s accuracy in predicting the behavior of the process and determining if any modifications need to be made to the model.
Examples
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Monte Carlo Simulation: A random process used to generate samples from a probability distribution in order to estimate the expected value of a function or a parameter.
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Bootstrapping: A method of estimating the parameters of a population by resampling observations from the sample with replacement.
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Markov Chains: A stochastic process where the future state of the system is determined by the current state and a transition probability matrix.
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Brownian Motion: A stochastic process that describes the random motion of particles in a fluid or gas due to their collisions with other particles.
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Poisson Process: A random process used to model the arrival of events or objects in a given time interval.