Diffusion process: Difference between revisions

Revision as of 22:54, 3 February 2024

Revision as of 03:23, 13 March 2024

Line 1:

{{Short description|Solution to a stochastic differential equation}}

{{for|the marketing term|Diffusion of innovations}}

{{one source |date=March 2024}}

In [[probability theory]] and [[statistics]], '''diffusion processes''' are a class of continuous-time [[Markov process]] with [[almost surely]] [[continuous function|continuous]] sample paths. Diffusion process is stochastic in nature and hence is used to model many real-life stochastic systems. [[Brownian motion]], [[reflected Brownian motion]] and [[Ornstein–Uhlenbeck processes]] are examples of diffusion processes. It is used heavily in [[statistical physics]], [[statistical analysis]], [[information theory]], [[data science]], [[Artificial neural network|neural networks]], [[finance]] and [[marketing]].

Line 19 ⟶ 20:

{{Differentiable computing}}

{{Stochastic processes|state=collapsed}}

{{probability-stub}}

[[Category:Markov processes]]

{{probability-stub}}