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

fYear

2010

fDate

16-18 April 2010

Firstpage

344

Lastpage

348

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICIME.2010.5477814

Filename

5477814