@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",
}