Title :
Parameter estimation based on MCMC methods in PM2.5 and traffic
Author :
Wang, Weiqiang ; Niu, Zhendong ; Zhao, Yumin ; Cao, Yujuan ; Zhao, Kun
Author_Institution :
Sch. of Comput. Sci., Beijing Inst. of Technol., Beijing, China
Abstract :
In this paper, We briefly present an overview of Markov chain Monte Carlo(MCMC), the MCMC method is studied with LA long beach air pollution PM 2.5 traffic from 2001 to 2007 observations. A linear regression model was built. We carried out statistical and graphical analysis and convergence diagnostics of Monte Carlo sampling output. The conclusion illustrated that the model fitting the datasets very significantly. This approach applies to a large class of utility functions and models for Air pollution and traffic.
Keywords :
Markov processes; Monte Carlo methods; aerosols; air pollution; convergence; parameter estimation; regression analysis; utility theory; AD 2001 to 2007; California; LA long beach air pollution; Markov chain Monte Carlo method; USA; convergence diagnostics; graphical analysis; linear regression model; parameter estimation; statistical analysis; traffic; utility function; Air pollution; Bayesian methods; Computational modeling; Linear regression; Monte Carlo methods; Parameter estimation; Sections; Statistical analysis; Statistical distributions; Traffic control; Bayesian modeling; Markov chain Monte Carlo; time series;
Conference_Titel :
Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5263-7
Electronic_ISBN :
978-1-4244-5265-1
DOI :
10.1109/ICIME.2010.5477814
