Editing Gauss–Markov process (section)

{{distinguish|text=the [[Gauss–Markov theorem]] of mathematical statistics}}

'''Gauss–Markov stochastic processes''' (named after [[Carl Friedrich Gauss]] and [[Andrey Markov]]) are [[stochastic process]]es that satisfy the requirements for both [[Gaussian process]]es and [[Markov process]]es.<ref name=Rasmussen2006>{{cite book|last=C. E. Rasmussen & C. K. I. Williams|title=Gaussian Processes for Machine Learning|date=2006|publisher=MIT Press|isbn=026218253X|page=Appendix B|url=http://www.gaussianprocess.org/gpml/chapters/RWB.pdf}}</ref><ref name=Lamon2008>{{cite book|last=Lamon|first=Pierre|title=3D-Position Tracking and Control for All-Terrain Robots|url=https://archive.org/details/dpositiontrackin00lamo|url-access=limited|date=2008|publisher=Springer|isbn=978-3-540-78286-5|pages=[https://archive.org/details/dpositiontrackin00lamo/page/n99 93]–95}}</ref> A stationary Gauss–Markov process is unique{{Citation needed|reason=here also some assumption is missing: a process with iid Gaussian values is Gauss-Markov|date=February 2019}} up to rescaling; such a process is also known as an [[Ornstein–Uhlenbeck process]].

Gauss–Markov processes obey [[Langevin equation]]s.<ref>{{cite book|author=Bob Schutz, Byron Tapley, George H. Born |title=Statistical Orbit Determination |date=2004-06-26 |isbn=978-0-08-054173-0 |pages=230|url=https://books.google.com/books?id=Ct3qN1VCHewC&q=Gauss%E2%80%93Markov+process+%22langevin+equation%22&pg=PA230}}</ref>