# Difference between revisions of "Aaron Clauset"

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Aaron Clauset [[http://www.santafe.edu/~aaronc/ homepage]] has been a post-doctoral fellow at the Santa Fe Institute since 2006 and is an alumnus of the CSSS 2003 (Santa Fe). | Aaron Clauset [[http://www.santafe.edu/~aaronc/ homepage]] has been a post-doctoral fellow at the Santa Fe Institute since 2006 and is an alumnus of the CSSS 2003 (Santa Fe). | ||

− | + | === Santa Fe School === | |

− | :"[[Media:CMN_08_Hierarchy_Preprint.pdf|Hierarchical structure and the prediction of missing links in networks. | + | This year, Clauset will deliver a lecture on Markov chain Monte Carlo methods for simulation and inference in complex systems. To be best prepared for the lecture, you should take a look at these papers. The first is the most relevant, with the others being more background material. |

+ | |||

+ | * [[Media:CMN_08_Hierarchy_Preprint.pdf|"Hierarchical structure and the prediction of missing links in networks." Clauset, Moore and Newman. <i>Nature</i> <b>453</b>, 98-101 (2008).]] | ||

+ | * [http://www-personal.umich.edu/~mejn/nbook/ <i>Monte Carlo Methods in Statistical Physics.</i> Newman and Barkema. Oxford University Press (1999).] | ||

+ | * [http://citeseer.ist.psu.edu/andrieu03introduction.html "An Introduction to MCMC for Machine Learning." Andrieu, de Freitas, Doucet and Jordan. <i>Machine Learning</i> <b>50</b>, 5-43 (2003).] | ||

+ | |||

+ | === Beijing School === | ||

+ | |||

+ | Clauset will deliver three lectures on topics including estimation and validation of power-law distributions in empirical data, Markov chain Monte Carlo methods for simulation an inference in complex systems, and hierarchical random graphs. To be best prepared for the lecture, you should take a look at these papers. The first two are the most relevant, with the others being more background material. | ||

+ | |||

+ | * [[Media:CSN_07_PowerlawDistributionsInEmpiricalData_arxiv.pdf|"Power-law distributions in empirical data." Clauset, Shalizi and Newman. arXiv:0706.1062 (2007).]] | ||

+ | * [[Media:CMN_08_Hierarchy_Preprint.pdf|"Hierarchical structure and the prediction of missing links in networks." Clauset, Moore and Newman. <i>Nature</i> <b>453</b>, 98-101 (2008).]] | ||

+ | * [http://www-personal.umich.edu/~mejn/nbook/ <i>Monte Carlo Methods in Statistical Physics.</i> Newman and Barkema. Oxford University Press (1999).] | ||

+ | * [http://citeseer.ist.psu.edu/andrieu03introduction.html "An Introduction to MCMC for Machine Learning." Andrieu, de Freitas, Doucet and Jordan. <i>Machine Learning</i> <b>50</b>, 5-43 (2003).] |

## Revision as of 14:30, 27 May 2008

Aaron Clauset [homepage] has been a post-doctoral fellow at the Santa Fe Institute since 2006 and is an alumnus of the CSSS 2003 (Santa Fe).

### Santa Fe School

This year, Clauset will deliver a lecture on Markov chain Monte Carlo methods for simulation and inference in complex systems. To be best prepared for the lecture, you should take a look at these papers. The first is the most relevant, with the others being more background material.

- "Hierarchical structure and the prediction of missing links in networks." Clauset, Moore and Newman.
*Nature***453**, 98-101 (2008). *Monte Carlo Methods in Statistical Physics.*Newman and Barkema. Oxford University Press (1999).- "An Introduction to MCMC for Machine Learning." Andrieu, de Freitas, Doucet and Jordan.
*Machine Learning***50**, 5-43 (2003).

### Beijing School

Clauset will deliver three lectures on topics including estimation and validation of power-law distributions in empirical data, Markov chain Monte Carlo methods for simulation an inference in complex systems, and hierarchical random graphs. To be best prepared for the lecture, you should take a look at these papers. The first two are the most relevant, with the others being more background material.

- "Power-law distributions in empirical data." Clauset, Shalizi and Newman. arXiv:0706.1062 (2007).
- "Hierarchical structure and the prediction of missing links in networks." Clauset, Moore and Newman.
*Nature***453**, 98-101 (2008). *Monte Carlo Methods in Statistical Physics.*Newman and Barkema. Oxford University Press (1999).- "An Introduction to MCMC for Machine Learning." Andrieu, de Freitas, Doucet and Jordan.
*Machine Learning***50**, 5-43 (2003).