Search results
Results from the WOW.Com Content Network
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.
The logistic distribution arises as limit distribution of a finite-velocity damped random motion described by a telegraph process in which the random times between consecutive velocity changes have independent exponential distributions with linearly increasing parameters.
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.
The telegrapher's equations (or just telegraph equations) are a set of two coupled, linear equations that predict the voltage and current distributions on a linear electrical transmission line. The equations are important because they allow transmission lines to be analyzed using circuit theory . [ 1 ]
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 .
Let {} be a random process, and be any point in time (may be an integer for a discrete-time process or a real number for a continuous-time process). Then X t {\\displaystyle X_{t}} is the value (or realization ) produced by a given run of the process at time t {\\displaystyle t} .
The task is to predict the efficacy of a given molecule for a specific medical application, like eliminating E. coli bacteria. The key design element of GNNs is the use of pairwise message passing , such that graph nodes iteratively update their representations by exchanging information with their neighbors.