Difference between revisions of "CSSS 2008 Santa Fe-Readings"
From Santa Fe Institute Events Wiki
(added references for Clauset's lecture) |
m |
||
Line 24: | Line 24: | ||
== Week Two: Ecology/Evolution/Molecular Biology/Disordered Systems == | == Week Two: Ecology/Evolution/Molecular Biology/Disordered Systems == | ||
− | ===Aaron Clauset: | + | ===Aaron Clauset: MCMC for Simulation and Inference === |
− | The following three references cover a wide variety of details related to Markov chain Monte Carlo methods. The second and third are general references, written for physics and machine learning audiences. (The Newman and Barkema book should be available through the SFI Library.) The | + | The following three references cover a wide variety of details related to Markov chain Monte Carlo (MCMC) methods. The second and third are general references, written for physics and machine learning audiences. (The Newman and Barkema book should be available through the SFI Library.) The first shows an application of MCMC methods in the context of learning the large-scale structure of networks. |
* [[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).]] | * [[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://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).] | * [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 14:43, 19 May 2008
CSSS Santa Fe 2008 |
Contents
Week One: Modeling/Nonlinear Dynamics
Liz Bradley: Non-Linear Dynamics I-IV
Nonlinear Dynamics
Owen Densmore & Steve Guerin: Modeling
Before the modeling class (afternoon the first day!) you should:
- Download the most recent versions of both NetLogo and NetLogo 3D from http://ccl.northwestern.edu/netlogo/
- Run some of the Model Library examples for both NetLogo and NetLogo 3D:
- Start the application
- Click Model Library in the File menu, try these:
- NetLogo: Art > Diffusion Graphics
- NetLogo 3D: 3D > Sample Models > Raindrops 3D
Note: move the 3D raindrop world by click & drag - To run most of the models, click "Setup" then "Go"
- To see the code, click on the Procedures tab
- Start the application
- Finally, under the "Help" menu, click "User Manual" and explore!
Week Two: Ecology/Evolution/Molecular Biology/Disordered Systems
Aaron Clauset: MCMC for Simulation and Inference
The following three references cover a wide variety of details related to Markov chain Monte Carlo (MCMC) methods. The second and third are general references, written for physics and machine learning audiences. (The Newman and Barkema book should be available through the SFI Library.) The first shows an application of MCMC methods in the context of learning the large-scale structure of networks.
- "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).