• Part of the 2009-10 SAMSI Program on Space-time Analysis for Environmental Mapping, Epidemiology and Climate Change
• Meeting time/location: Tuesday, 9:00am - 10:45am, SAMSI, Room 203
• Leaders: Murali Haran and Susie Bayarri
• Webmaster: Emily L. Kang
• Wiki manual: DokuWiki and its syntax

Please login (upper-left button) to access the private pages and copyrighted articles of this wiki.

Next meeting will be on Wednesday, 2/23/2011.

• Time and location: 12:15 pm – 2:00 pm (EST), Webex

1. Fully Bayesian inference for models that capture the dynamics of alcohol drinking levels, focusing on the importance of accounting for biases, structural discrepancies in the model for dynamics, and multiple sources of uncertainty. The model and data set are as presented by Anuj Mubayi in the data assimilation working group meetings. This is a collaboration between the stochastic dynamics and spatial models research programs.
   • Members: (1) Susie Bayarri, (2) Anabel Forte, (3) Anuj Mubayi and (4) Cindy Greenwood
2. Statistical inference for dynamics models for the spread of dengue fever, using a very rich spatio-temporal data set on dengue in Peru as motivation. The model and data set are as presented by Carlos Torres in our working group meeting in October.
   • Members: (1) Carlos Torres, (2) Cindy Greenwood, (3) Murali Haran, (4) Sourish Das
3. Simplifications of the spatial disease dynamics models presented by Carlos Torres, as suggested by mathematical theory. A discussion on this was begun by Peter Kramer, John Harlim and others in the working group meeting in October. Ilya Timofoyev from the University of Houston will speak on related issues during the working group meeting on November 3rd.
   • Members: (1) Peter Kramer, (2) John Harlim, (3) Ilya Timofoyev, (4) Cindy Greenwood, (5) Carlos Torres
4. Using Gaussian processes to study multiscale dynamical systems. The idea was to use GP models to reconstruct the smaller scale processes. Since the large scale processes are typically measurable from nature, real time prediction on the large scale is not a problem. The problem is typically on resolving the smaller scale which is expensive even with numerical simulations. A lot of the time, these subgrid physical processes are parameterized. John Harlim suggests starting with a test model for which dynamical properties are fully understood, if it works, then the model may be made more complicated by adding more relevant smaller scale physical processes. Susie and Jim suggested that there may also be some interest in exploring dynamical emulation ideas to work on this problem.
   • Members: (1) John Harlim, (2) Peter Kramer, (3) Jim Berger, (4) Susie Bayarri, (6) Emily L. Kang

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E-mail alias for the group is sp-stats AT samsi.info.

1. Chowell G, Torre CA, Munayco-Escate C, Suárez-Ognio L, López-Cruz R, Hyman JM, Castillo-Chavez C, (2008) Spatial and temporaldynamics of dengue fever in Peru: 1994–2006, Epidemiol Infect., 136(12), 1667–1677. [pdf]
2. Weinan E, (2009) HMM and the ``equation-free'' approach to multiscale modeling, preprints, Princeton University. [pdf]
3. Weinan E, Weiqing Ren, and Eric Vanden-Eijnden, (2009) A general strategy for designing seamless multiscale methods, Journal of Computational Physics, 228, 5437–5453. [pdf]
4. Ibrahim Fatkullin, Eric Vanden-Eijnden, (2004) A computational strategy for multiscale systems with applications to Lorenz 96 model, Journal of Computational Physics, 200, 605–638. [pdf]
5. Lewis MA and Pacala S., (2000) Modeling and analysis of stochastic invasion processes, Journal of Mathematical Biology, 41, 387–429. [bib | pdf ]
6. Sorin Mitran, (2010) Time parallel kinetic-molecular interaction algorithm for CPU/GPU computers,Procedia Computer Science, 00, 1–10. [pdf]
7. Albert Tarantola, (2004) Inverse Problem Theory and Methods for Model Parameter Estimation Click here
8. Sorin Mitran and Jennifer Young, (2010) Multiscale computation of cytoskeletal mechanics during blebbing, submitted pdf
9. R. Byron Bird, Robert C. Armstrong, and Ole Hassager, Dynamics of Polymer Liquids, Secs. 5.1-5.3, 11.5, 13.1-13.5 pdf
10. R. G. Owens and T. N. Phillips, Computational Rheology, Sec. 2.6, 11.1-11.4 pdf

1. NOAA Hurricane track for Atlantic and East North Pacific basin Click here
2. NCDC: Online Climate Data Directory Click here
3. Climate Data Archieve Click here
4. Hurricane/Tropical Data Click here
5. Average Daily Temperature Archive Click here
6. International Disaster Database Click here
7. USGS Water Data base Click here
8. EPA Data Archieve Click here

1. Journal of Statistical Education – Data Archive: Click here
2. StatLib – JASA Data Archive Click here
3. Chandra Data Archive Click here
4. Public Opinion: Roper Center, University of Connecticut: Click here

1. Analysis of spatial data using R Click here
2. Time series analysis using R Click here
3. “splancs” is an R package on spatial and space-time point pattern analysis. Click here.
4. R packages on Bayesian inference Click here
5. “RAMPS” is a new R package on unified Bayesian geostatistical modeling of complex spatiotemporal data. Read the news about the package in ISBA bulletin at page 12-15. Click here You can download the package from the following link. Click here

1. Go to the email invitation that you received announcing the meeting, or go directly to WebEx: https://samsi.webex.com
2. Click on the link for the meeting you want to join
   • Enter Your Name, Your Email Address and the meeting password from the email invitation
   • The first time you use WebEx, the Meeting Manager will run and it will take about a minute to setup.
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3. Dial the teleconference line: 919-685-9366
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You can learn more about WebEx and take tutorials at http://university.webex.com.