Difference between revisions of "Stochastic Process with Memory"
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For a review of memory in a stochastic process. The “specialness” of long memory indicates that most stationary stochastic processes do not have it. <ref>GENNADY SAMORODNITSKY ''LONG RANGE DEPENDENCE'' Cornell (2007?) https://people.orie.cornell.edu/gennady/techreports/LRD-NOW.pdf</ref> | For a review of memory in a stochastic process. The “specialness” of long memory indicates that most stationary stochastic processes do not have it. <ref>GENNADY SAMORODNITSKY ''LONG RANGE DEPENDENCE'' Cornell (2007?) https://people.orie.cornell.edu/gennady/techreports/LRD-NOW.pdf</ref> | ||
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| + | A stationary process is a stochastic process whose unconditional joint probability distribution does not change when shifted in time. | ||
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==Context== | ==Context== | ||
The first serious argument that this can be important is | The first serious argument that this can be important is | ||
Revision as of 09:57, 29 November 2024
Full Title
For a review of memory in a stochastic process. The “specialness” of long memory indicates that most stationary stochastic processes do not have it. [1]
A stationary process is a stochastic process whose unconditional joint probability distribution does not change when shifted in time.
Context
The first serious argument that this can be important is in a series of papers of B. Mandelbrot and his co-authors, e.g. Mandelbrot (1965) and Mandelbrot and Wallis (1968). It is also due to the influence of these early papers and subsequent publications of Mandelbrot (especially Mandelbrot (1983)) that long range dependence has also become associated with scaling and fractal behavior.
References
- ↑ GENNADY SAMORODNITSKY LONG RANGE DEPENDENCE Cornell (2007?) https://people.orie.cornell.edu/gennady/techreports/LRD-NOW.pdf
