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 University, Columbus, OH 43210 This contribution is part of
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 robust and efficient mechanisms for keeping nodes upda
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 Bayesian variable selection, despite several undesirable co
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 one agent is Infected, others are Susceptible.
When an I
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.
I can remember Bertrand Russell telling me of a horribl
X'dcfw_ylcfw_ cfw_jvx cfw_x
t
2
) r
U pT7Usq
R 70 kk
v cfw_ C 9d2 cfw_w
8 pTAw w p d vCEx vdlu uXsq
S r v
t r
v R d x cfw_jvx1p vdlih
tf8 v DgfXdv ` 8 lF w cI
e
d I e d
2
cfw_ ca 'FvYdFE f W
b ` X
R I
ax V tU p p lcfw_vcfw_ fT
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
(statistical physics and interacting particle systems; epidemic th
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 mixing times.
Three standard examples.
Other examples.
Only
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 agents are infected. Whenever an infected agent
meets anot
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 statements which only depend on the assumed model and
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 probability distributions f n gn 1 dened on a
sequence of
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.
There is a large, popular, and growing literature on
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 acquaintances - was inaugurated as an area of experimen
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, and show that the mixing time of this Markov chain
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 , . . .) of a graph specifies for each possible deg
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 extinction and survival probabilities expressed i
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 distribution
qn (x1 :n ) = qn
(x1 :n 1 ) qn ( xn j x1
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
combined money equally, so the values (Xi (t) and Xj (