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Developmental noise or stochastic noise is a concept within developmental biology in which the observable characteristics or traits varies between individuals even though both individuals share the same genetic code and the other environmental factors are completely the same.
Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. Examples of such stochastic processes include the Wiener process or Brownian motion process, [a] used by Louis Bachelier to study price changes on the Paris Bourse, [21] and the Poisson process, used by A. K. Erlang to study the number of ...
Stochastic music was pioneered by Iannis Xenakis, who coined the term stochastic music. Specific examples of mathematics, statistics, and physics applied to music composition are the use of the statistical mechanics of gases in Pithoprakta, statistical distribution of points on a plane in Diamorphoses, minimal constraints in Achorripsis, the ...
Branching processes can be simulated for a range of problems. One specific use of simulated branching process is in the field of evolutionary biology. [5] [6] Phylogenetic trees, for example, can be simulated under several models, [7] helping to develop and validate estimation methods as well as supporting hypothesis testing.
A canonical model for stochastic gene expression, known as the two-state or telegraph [30] model. DNA flips between "inactive" and "active" states (involving, for example, chromatin remodelling and transcription factor binding). Active DNA is transcribed to produce mRNA which is translated to produce protein, both of which are degraded.
Stochastic resonance is a phenomenon that occurs in a threshold measurement system (e.g. a man-made instrument or device; a natural cell, organ or organism) when an appropriate measure of information transfer (signal-to-noise ratio, mutual information, coherence, d', etc.) is maximized in the presence of a non-zero level of stochastic input noise thereby lowering the response threshold; [1 ...
Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.
A Moran process or Moran model is a simple stochastic process used in biology to describe finite populations. The process is named after Patrick Moran, who first proposed the model in 1958. [1] It can be used to model variety-increasing processes such as mutation as well as variety-reducing effects such as genetic drift and natural selection.