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Difference between revisions of "Aaron Clauset"

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=== Beijing School ===
 
=== 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.
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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 and 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:CSN_07_PowerlawDistributionsInEmpiricalData_arxiv.pdf|"Power-law distributions in empirical data." Clauset, Shalizi and Newman. arXiv:0706.1062 (2007).]]

Revision as of 17:25, 30 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.

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 and 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.