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Perlin noise is a type of gradient noise developed by Ken Perlin in 1983. It has many uses, including but not limited to: procedurally generating terrain, applying pseudo-random changes to a variable, and assisting in the creation of image textures. It is most commonly implemented in two, three, or four dimensions, but can be defined for any ...
A geometric Brownian motion (GBM) (also known as exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion (also called a Wiener process) with drift. [1] It is an important example of stochastic processes satisfying a stochastic differential equation ...
A Binomial distributed random variable X ~ B(n, p) can be considered as the sum of n Bernoulli distributed random variables. So the sum of two Binomial distributed random variable X ~ B(n, p) and Y ~ B(m, p) is equivalent to the sum of n + m Bernoulli distributed random variables, which means Z=X+Y ~ B(n+m, p). This can also be proven directly ...
A spreadsheet is a computer application for computation, organization, analysis and storage of data in tabular form. [1][2][3] Spreadsheets were developed as computerized analogs of paper accounting worksheets. [4] The program operates on data entered in cells of a table. Each cell may contain either numeric or text data, or the results of ...
The formula was defined by Jeff Tupper and appears as an example in Tupper's 2001 SIGGRAPH paper on reliable two-dimensional computer graphing algorithms. [1] This paper discusses methods related to the GrafEq formula-graphing program developed by Tupper. [2] Although the formula is called "self-referential", Tupper did not name it as such. [3]
Pseudorandomness. A pseudorandom sequence of numbers is one that appears to be statistically random, despite having been produced by a completely deterministic and repeatable process. [1] Simply put, the problem is that many of the sources of randomness available to humans (such as rolling dice) rely on physical processes not readily available ...
Gaussian noise. In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). [1][2] In other words, the values that the noise can take are Gaussian-distributed.
Frank Guzman and his wife Caroline Katba were traveling in Mexico when family, friends lost contact with them around July 22, Guzman's sibling said.