Editing Monte Carlo method (section)

===Engineering===
Monte Carlo methods are widely used in engineering for [[sensitivity analysis]] and quantitative [[probabilistic]] analysis in [[Process design (chemical engineering)|process design]]. The need arises from the interactive, co-linear and non-linear behavior of typical process simulations. For example,
* In [[microelectronics|microelectronics engineering]], Monte Carlo methods are applied to analyze correlated and uncorrelated variations in [[Analog signal|analog]] and [[Digital data|digital]] [[integrated circuits]].
* In [[geostatistics]] and [[geometallurgy]], Monte Carlo methods underpin the design of [[mineral processing]] [[process flow diagram|flowsheets]] and contribute to [[quantitative risk analysis]].{{sfn|Mazhdrakov|Benov|Valkanov|2018|p=250}}
* In [[fluid dynamics]], in particular [[gas dynamics|rarefied gas dynamics]], where the Boltzmann equation is solved for finite [[Knudsen number]] fluid flows using the [[direct simulation Monte Carlo]]<ref>G. A. Bird, Molecular Gas Dynamics, Clarendon, Oxford (1976)</ref> method in combination with highly efficient computational algorithms.<ref>{{cite journal |author-last1=Dietrich |author-first1=S. |author-last2=Boyd |author-first2=I. |year=1996 |title=A Scalar optimized parallel implementation of the DSMC technique |journal=Journal of Computational Physics |volume=126 |issue=2 |pages=328–42 |doi=10.1006/jcph.1996.0141 |bibcode=1996JCoPh.126..328D |doi-access=free }}</ref>
* In [[autonomous robotics]], [[Monte Carlo localization]] can determine the position of a robot. It is often applied to stochastic filters such as the [[Kalman filter]] or [[particle filter]] that forms the heart of the [[Simultaneous localization and mapping|SLAM]] (simultaneous localization and mapping) algorithm.
* In [[telecommunications]], when planning a wireless network, the design must be proven to work for a wide variety of scenarios that depend mainly on the number of users, their locations and the services they want to use. Monte Carlo methods are typically used to generate these users and their states. The network performance is then evaluated and, if results are not satisfactory, the network design goes through an optimization process.
* In [[reliability engineering]], Monte Carlo simulation is used to compute system-level response given the component-level response.
* In [[signal processing]] and [[Bayesian inference]], [[particle filter]]s and [[sequential Monte Carlo method|sequential Monte Carlo techniques]] are a class of [[mean-field particle methods]] for sampling and computing the posterior distribution of a signal process given some noisy and partial observations using interacting [[empirical measure]]s.<ref>{{cite journal |author-last1=Chen |author-first1=Shang-Ying |author-last2=Hsu |author-first2=Kuo-Chin |author-last3=Fan |author-first3=Chia-Ming |title=Improvement of generalized finite difference method for stochastic subsurface flow modeling |journal=Journal of Computational Physics |date=March 15, 2021 |volume=429 |pages=110002 |doi=10.1016/J.JCP.2020.110002 |bibcode=2021JCoPh.42910002C |s2cid=228828681 }}</ref>