COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Probability > Percolation of random interlacements under small intensities

## Percolation of random interlacements under small intensitiesAdd to your list(s) Download to your calendar using vCal - Augusto Teixeira (ETH-Zürich)
- Wednesday 02 December 2009, 16:00-17:00
- Newton Institute Gatehouse.
If you have a question about this talk, please contact Julia Blackwell. The model of random interlacements was recently introduced by Alain-Sol Sznitman as a natural tool to understand the trace left by a random walk in a discrete cylinder or in a discrete torus. In these contexts, this model describes the microscopic “texture in the bulk” left by the random walk when it is let run up to certain time scales. In this talk we are going to discuss some percolative properties of the vacant set of random interlacements under small intensities (e.g. the size of a finite vacant cluster). The results which will be presented could shed some light on problems such as how a random walk trajectory disconnects a discrete cylinder into two infinite connected components. This talk is part of the Probability series. ## This talk is included in these lists:- All CMS events
- All Talks (aka the CURE list)
- CMS Events
- DPMMS Lists
- DPMMS info aggregator
- DPMMS lists
- Newton Institute Gatehouse
- Probability
- School of Physical Sciences
- Statistical Laboratory info aggregator
Note that ex-directory lists are not shown. |
## Other listsBiophysical Seminars Cambridge University Surgical Society National Biology Week talks## Other talksInnate Immunity: The first line of defence CO2 + H2O + Sunlight → Chemical Fuels + O2 Centre for Industrial Sustainability 5th Annual Conference - Capturing Sustainable Value Structure, Biology and Therapeutic Potential of Novel ER Located Growth Factors Tutorial 1: Data Linkage – Introduction, Recent Advances, and Privacy Issues Using Differential Privacy to Control False Discovery in Adaptive Data Analysis |