Parameter estimation based on MCMC methods in PM2.5 and traffic

Weiqiang Wang; Zhendong Niu; Yumin Zhao; Yujuan Cao; Kun Zhao

doi:10.1109/ICIME.2010.5477814

Parameter estimation based on MCMC methods in PM2.5 and traffic

Weiqiang Wang^*, Zhendong Niu, Yumin Zhao, Yujuan Cao, Kun Zhao

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering
Pages	344-348
Number of pages	5
DOIs	https://doi.org/10.1109/ICIME.2010.5477814
Publication status	Published - 2010
Event	2010 2nd IEEE International Conference on Information Management and Engineering, ICIME 2010 - Chengdu, China Duration: 16 Apr 2010 → 18 Apr 2010

Publication series

Name	ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering
Volume	5

Conference

Conference	2010 2nd IEEE International Conference on Information Management and Engineering, ICIME 2010
Country/Territory	China
City	Chengdu
Period	16/04/10 → 18/04/10

Keywords

Bayesian modeling
Markov chain Monte Carlo
Time series

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10.1109/ICIME.2010.5477814

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Wang, W., Niu, Z., Zhao, Y., Cao, Y., & Zhao, K. (2010). Parameter estimation based on MCMC methods in PM2.5 and traffic. In ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering (pp. 344-348). Article 5477814 (ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering; Vol. 5). https://doi.org/10.1109/ICIME.2010.5477814

@inproceedings{b84b99f5d15f4f11be75649c3445fc3e,

title = "Parameter estimation based on MCMC methods in PM2.5 and traffic",

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 = "Bayesian modeling, Markov chain Monte Carlo, Time series",

author = "Weiqiang Wang and Zhendong Niu and Yumin Zhao and Yujuan Cao and Kun Zhao",

year = "2010",

doi = "10.1109/ICIME.2010.5477814",

language = "English",

isbn = "9781424452644",

series = "ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering",

pages = "344--348",

booktitle = "ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering",

note = "2010 2nd IEEE International Conference on Information Management and Engineering, ICIME 2010 ; Conference date: 16-04-2010 Through 18-04-2010",

}

Wang, W, Niu, Z, Zhao, Y, Cao, Y & Zhao, K 2010, Parameter estimation based on MCMC methods in PM2.5 and traffic. in ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering., 5477814, ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering, vol. 5, pp. 344-348, 2010 2nd IEEE International Conference on Information Management and Engineering, ICIME 2010, Chengdu, China, 16/04/10. https://doi.org/10.1109/ICIME.2010.5477814

Parameter estimation based on MCMC methods in PM2.5 and traffic. / Wang, Weiqiang; Niu, Zhendong; Zhao, Yumin et al.
ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering. 2010. p. 344-348 5477814 (ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Niu, Zhendong

AU - Zhao, Yumin

AU - Cao, Yujuan

AU - Zhao, Kun

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Bayesian modeling

KW - Markov chain Monte Carlo

KW - Time series

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T3 - ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering

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BT - ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering

T2 - 2010 2nd IEEE International Conference on Information Management and Engineering, ICIME 2010

Y2 - 16 April 2010 through 18 April 2010

ER -

Parameter estimation based on MCMC methods in PM2.5 and traffic

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Keywords

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