A dynamic model of social network formation
Brian Skyrms* and Robin Pemantle
*School of Social Sciences, University of California, Irvine, CA 92607; and Department of Mathematics, Ohio State Universit
Spatial Gossip and Resource Location Protocols
David Kempe Jon Kleinberg
Alan Demers
Abstract The dynamic behavior of a network in which information is changing continuously over time requires robu
Mixtures of g Priors for Bayesian Variable Selection
Feng L IANG, Rui PAULO, German M OLINA, Merlise A. C LYDE, and Jim O. B ERGER
Zellners g prior remains a popular conventional prior for use in Baye
Finite Markov Information-Exchange processes
David Aldous
March 17, 2011
What we have called the simple epidemic process could also be called the
SI process, writing the evolution rules as
Initially o
Finite Markov Information-Exchange processes
David Aldous
March 30, 2011
Note that previous FMIE models were non-equilibrium. We digress to a
quite dierent FMIE model designed to have an equilibrium.
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Finite Markov Information-Exchange processes
David Aldous
February 2, 2011
Course web site: Google Aldous STAT 260.
Style of course
Big Picture thousands of papers from dierent disciplines
(statistica
Finite Markov Information-Exchange processes
David Aldous
February 2, 2011
Markov Chains
The next few lectures give a brisk discussion of
Basics: discrete- and continuous-time.
Hitting times and mixin
Finite Markov Information-Exchange processes
David Aldous
March 8, 2011
The simple epidemic model as a FMIE.
General underlying meeting model parametrized by rates N = (ij ).
Initially one or more age
Reference Analysis
Jos M. Bernardo 1
e
Departamento de Estad
stica e I.0., Universitat de Val`ncia, Spain
e
Abstract
This chapter describes reference analysis, a method to produce Bayesian inferential
Importance Sampling & Sequential Importance Sampling
Arnaud Doucet
Departments of Statistics & Computer Science
University of British Columbia
A.D. ()
1 / 40
Generic Problem
Consider a sequence of pro
Internet Mathematics Vol. 2, No. 4: 431-523
Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications
Lun Li, David Alderson, John C. Doyle, and Walter Willinger
Abstract.
The
The Small-World Phenomenon: An Algorithmic Perspective
Jon Kleinberg
Abstract Long a matter of folklore, the "small-world phenomenon" - the principle that we are all linked by short chains of acqua
Sampling regular graphs and a peer-to-peer network
(Extended Abstract) Colin Cooper Martin Dyerand Catherine Greenhill ,
Abstract We consider a simple Markov chain for d-regular graphs on n vertices,
Stat206b Random Graphs
Spring 2003
Lecture 3: January 28
Lecturer: David Aldous Scribe: Samantha Riesenfeld
Random graphs with a prescribed degree distribution
A degree distribution (d0 , d1 , d2 ,
Stat206: Random Graphs and Complex Networks
Spring 2003
Lecture 2: Branching Processes
Lecturer: David Aldous Scribe: Lara Dolecek
Today we will review branching processes, including the results on
Sequential Importance Sampling Resampling
Arnaud Doucet
Departments of Statistics & Computer Science
University of British Columbia
A.D. ()
1 / 30
Sequential Importance Sampling
We use a structured IS
Finite Markov Information-Exchange processes
David Aldous
February 9, 2011
Model 2. Averaging model. Here information is most naturally
interpreted as money. When agents i and j meet, they split their