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 |
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| Title of host publication | ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering |
| Pages | 344-348 |
| Number of pages | 5 |
| DOIs | |
| 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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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