On Monte Carlo methods for Bayesian inference

By Song S. Qian, Craig A. Stow, Mark E. Borsuk. 2003

192 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bfcdc71b8bb9446cfe988a601520e32ee048d7a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Chemistry

1. Yinphan Tsang
2. Renate Meyer
3. Michael Dowd
4. Fu‐Chun Wu

In Environmental Engineering

1. Michael Dowd
2. Yinphan Tsang
3. Renate Meyer
4. Fu‐Chun Wu

In Statistics and Probability

1. Yinphan Tsang
2. Renate Meyer
3. Michael Dowd
4. Huaicheng Guo
5. Yong Liu
6. Fu‐Chun Wu
7. Pingjian Yang
8. Guozhu Mao

Citations in the corpus

• Techniques for cetacean-habitat modeling (2006, unknown): “For example, Qian et al. (2003) discuss using Markov chain Monte Carlo simulation techniques to solve the difficult problem of sampling from a highdimensional distribution, which arises for models with a large number of variables.”
• Second-order Monte Carlo uncertainty/variability analysis using correlated model parameters: application to salmonid embryo survival risk assessment (2004, methodology): “Monte Carlo methods have been widely used in the context of ecological modeling (Neave et al., 1997; Harmon and Challenor, 1997; Annan, 2001; Qian et al., 2003; Waller et al., 2003).”
• A Bayesian hierarchical model for urban air quality prediction under uncertainty (2008, methodology): “The Monte Carlo method is widely used to assist with numerical summarization in Bayesian methods; and the Markov Chain Monte Carlo (MCMC) is a popular Monte Carlo algorithm (Qian et al., 2003).”
• A Bayesian approach to the ecosystem inverse problem (2003, methodology): “Applications of Bayesian methods to ecosystem models have to date been focused on the estimation of relatively small number of parameters (Omlin and Reichert, 1999; Borsuk et al., 2001; Qian et al., 2003) .”