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This random process finds wide application in model building: In physics , spin systems and fluorescence intermittency show dichotomous properties. But especially in single molecule experiments probability distributions featuring algebraic tails are used instead of the exponential distribution implied in all formulas above.
It is also called random telegraph noise (RTN), popcorn noise, impulse noise, bi-stable noise, or random telegraph signal (RTS) noise. It consists of sudden step-like transitions between two or more discrete voltage or current levels, as high as several hundred microvolts , at random and unpredictable times.
Financial signal processing is a branch of signal processing technologies which applies to signals within financial markets. They are often used by quantitative analysts to make best estimation of the movement of financial markets , such as stock prices, options prices, or other types of derivatives .
In the theory of stochastic processes, filtering describes the problem of determining the state of a system from an incomplete and potentially noisy set of observations. . While originally motivated by problems in engineering, filtering found applications in many fields from signal processing to fi
In mathematics, random graph is the general term to refer to probability distributions over graphs. Random graphs may be described simply by a probability distribution, or by a random process which generates them. [1] [2] The theory of random graphs lies at the intersection between graph theory and probability theory.
A random vector (that is, a random variable with values in R n) is said to be a white noise vector or white random vector if its components each have a probability distribution with zero mean and finite variance, [clarification needed] and are statistically independent: that is, their joint probability distribution must be the product of the ...
Many AI platforms use Wikipedia data, [273] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, [ 274 ] detecting covert vandalism [ 275 ] or recommending articles and tasks to new editors.
In the statistical analysis of time series, autoregressive–moving-average (ARMA) models are a way to describe a (weakly) stationary stochastic process using autoregression (AR) and a moving average (MA), each with a polynomial. They are a tool for understanding a series and predicting future values.