CUNY Probability
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October 20, 2009 Ron Peled

Gravitational Allocation to Poisson Points


Concerning references, the paper is based on the following two articles on gravitational allocation (both on the Arxiv):
Chatterjee Sourav, Peled Ron, Peres Yuval and Romik Dan. Gravitational allocation to Poisson points. To appear in Annals of Mathematics.
Chatterjee Sourav, Peled Ron, Peres Yuval and Romik Dan. Phase Transitions in Gravitational Allocation.

These articles, in turn, borrow many ideas from the seminal work:
Nazarov Fedor, Sodin Mikhail, Volberg Alexander. Transportation to
random zeroes by the gradient flow. Geometric and Functional Anal-
ysis Vol 17-3, 887-935, 2007 (An older version 1 can be found in
http://www.arxiv.org/abs/math/0510654v1 .)

The idea for the gravitational allocation was proposed in:
Sodin Mikhail and Tsirelson Boris. Random complex zeroes II: Perturbed
lattice. Israel J. Math. 152 (2006), 105–124.

Our analysis in the second paper requires the understanding of a special type of Quadrature Formulas, the so called Chebyshev-type quadrature formulas. They are surveyed and some facts developed for them in:
Peled Ron. Simple Universal Bounds for Chebyshev-Type Quadratures.



Ajtai, Komlos and Tusnady (1984) showed that any matching between n uniform independent points in the square of sidelength sqrt(n) and a lattice with n points in this square, will necessarily have average matching distance growing at least like sqrt(log(n)). Leighton and Shor (1986) analyzed the same problem with worst matching distance replacing average matching distance and obtained log(n)^(3/4). Talagrand (1994) continued on their work and showed that the behavior in 3 dimensions and higher is much better. He gave very precise control on the best matchings.

Holroyd and Peres (1995) showed equivalence of equivariant allocations and "extra head rules" (which are an interesting concept in their own right).
In the same paper they introduced the first deterministic equivariant allocation - the stable marriage of Poisson and Lebesgue. Later analyzed more in a joint paper with Hoffman.

Liggett (2000) and Holroyd and Liggett (2001) showed that any equivariant allocation for the Poisson process in dimensions 1 and 2 will necessarily have cells with high diameter with density which is only polynomially small. This is similar to the situation in finite volume that the works cited above of Ajtai,Komlos and Tusnady, Leighton and Schor and Talagrand explore.

Adam Timar (Invariant matchings of exponential tail on coin flips in Z^d, 2009) shows that one can have equivariant matchings with excellent tail bounds for the matching distance, when matching open and closed sites of a percolation on Z^d, d>=3, or when matching points of a Poisson process in 3 dimensions and higher. The method here is completely different than in the other papers.

Nice pictures and some brief explanations can be found on my homepage at:
http://www.cims.nyu.edu/~peled/allocations.html (there are also nice pictures on the main homepage)
and on Dan Romik's homepage at:
http://www.math.ucdavis.edu/~romik/home/Allocations.html

 David Mason


Self-Normalized Processes
Limit Theory and Statistical Applications

I have attached these chapters as pdf files, which I downloaded from the University of Washington library. (http://www.math.csi.cuny.edu/probability//Files/DavidMason-F09/)

For basic information about Levy processes I suggest a look at Chapter 15 of Foundations of Modern Probability by
by Olav Kallenberg. Also the first two sections of each of the attached Maller and Mason papers should helpful too.

Elena Kosygina, Oct. 27 2009


There are very few prerequisites one needs for this talk. I shall explain the model and some of the basics. One really useful thing to look up would be the correspondence between branching processes and random walks. For example (just to keep the same notation),
one can look at our preprint (on which the talk will be based): arXiv:0908.4356v1 [math.PR], Sections 1 and 2.
 
Tuesday, September 22, 4:00 PM, Rm. 5417. 
Speaker:  Fredrik Johansson, KTH
Title:  Optimal Holder exponent for the SLE path


References for graduate students (the first is a good introduction to SLE):

Random planar curves and Schramm-Loewner evolutions (Werner): http://arxiv.org/abs/math/0303354

Optimal Holder exponent for the SLE path (Johansson&Lawler): http://arxiv.org/abs/0904.1180

Mulltifractal analysis of the reverse flow for the Schramm-Lowener evolution (Lawler): http://www.math.uchicago.edu/~lawler/fracvol.pdf


May 12, 2009
Speaker: David Nualart, University of Kansas
Title:  
Central limit theorems for functionals of Gaussian processes.

References related to the talk:

1. D. Nualart and S. Ortiz-Latorre: Central limit theorems for multiple stochastic integrals and Malliavin calculus.  Stochastic Processes and their Applications, 118  (2008) 614--628.

2. D. Nualart  and G. Peccati: Central limit theorems for sequences  of multiple stochastic integrals.  Annals of Probability, 33  (2005) 177--193.

3. S. Darses, I. Nourdin and D. Nualart: Limit theorems for nonlinear functionals of Volterra processes via white noise analysis. Preprint 
http://arxiv.org/pdf/0904.1401.

4. D. Nualart: Stochastic integration with respect to fractional Brownian motion and applications. Contemp. Math., 336 (2003) 3--39. 

April 21, 2009 4pm
Speaker: Souvik Ghosh, Columbia University
Title:  
LARGE DEVIATION PRINCIPLE FOR A CLASS OF LONG RANGE DEPENDENT INFINITELY DIVISIBLE PROCESS
References:
 
 Alparslan, U.T., Samorodnitsky, G. (2007) {\sl Ruin probability with certain stationary stable claims generated by conservative flows\/},
Advances in Applied Probability. 39, 360--384.


  Ghosh, S. (2008){\sl The effect of memory on large deviations of moving average processes and infinitely divisible processes  \/}, Thesis, Cornell University.

  Mikosch, T., Samorodnitsky, G. (2000) {\sl The supremum of a negative drift random walk with dependent heavy-tailed steps\/}, 
The Annals of Probability.  10, 1025--1064.

  Rosinski, J., Samorodnitsky, G. (1996) {\sl Classes of mixing stable processes \/}, Bernoulli. 2, 365--377.


March 31, 2009 at 4:00 PM
Speaker: Jose Blanchet, Columbia University
Title:  Algorithms and Large Deviations

Abstract:        This talk concentrates on the interplay between large
deviations theory and the design of efficient stochastic simulation
algorithms that are aimed at both estimating rare-event probabilities
and sampling stochastic processes conditional on a rare event. The
point is designing simulation estimators that can be easily
implemented and whose coefficient of variation remains uniformly
bounded as the event becomes rarer and rarer. Typically, a large
deviations result is helpful to guide the construction of the
estimator, but, as we shall see, the complexity analysis of the
algorithm often demands a refinement of the underlying large
deviations argument behind the rare-event probability of interest. In
this talk we illustrate the techniques both in light and heavy-tailed
stochastic processes. Applications to stochastic networks will be
given as motivation.


Here are some useful refrences:
 
 Useful background references are the following. The first is a
paper joint paper with Jingchen and the second is not my paper but it
was among the first papers of Paul Dupuis and his co-authors that
dealt with this problem. In the talk I will present an approach that
is different from that of Paul, but this would do for background on
the problem. By the time of the talk it is very likely that a new
paper will be posted on my webpage related to the stuff on light
tails. I'll point to that then.

Heavy tails:   Rare-event Simulation for Multidimensional Regularly
Varying Random Walks (with J. C. Liu).
       
http://www.people.fas.harvard.edu/~blanchet/papers/HighDimJournal6.pdf

Light tails:     Dynamic importance sampling for queueing networks.
Paul Dupuis, Ali Devin Sezer, and Hui Wang: Ann. Appl. Probab. Volume
17, Number 4 (2007),  1306-1346.


February 24, 2009 Dmitry Dolgopyat, University of Maryland

The paper is available at arXiv:0806.3236
The background information can be found in the book
David Ruelle Thermodynamic Formalism: The Mathematical Structure of Equilibrium Statistical Mechanics (Cambridge Mathematical Library),
Chapter 5.


December 9, 2008 Julien Dubedat

 The talk is based on the introductory text  http://arxiv.org/pdf/math/0310326v1
Pawel Hitczenko, November 11, 2008
 The talk is based on the paper available at:

http://www.math.drexel.edu/~phitczen/perpdraft4.pdf

which has some references. In particular, Vervaat's paper is a good
starting point for students.
Jingchen Liu, September 23, 2008
 For students (and others) interested in material for this talk, please consult:

http://stat.columbia.edu/~jcliu/paper/SimGG1Final2nd.pdf

which is a single-
server version of the talk, but under the same principle.
Peter Carr, March 4 2008 
           On Schr¨dinger’s equation, 3-dimensional Bessel
                       o
                    bridges, and passage time problems
Abstract: The main aim of this work is finding an explicit representation of the
density ϕf of the first time T that a one-dimensional Brownian process B reaches
the moving boundary f , where
                                                      t
                                    f (t) := a +        f (s)ds
                                                    0
and
                                  T := inf{t ≥ 0|Bt = f (t)}
given that f (t) > 0, ∀t ≥ 0. We do so, by first finding the expected value of the
                                             ˜
following 3-dimesional Bessel bridge X functional
                                                s
                               E exp −                    ˜
                                                  f (u)Xu du     ,
                                              0
[the reader may consult for instance Chapter 11 in Revuz and Yor (2005) for a
general overview of this process] and exploiting its relationship with first-passage
time problems as pointed out by Kardaras (2007). It turns out that this problem
is related to Schr¨dinger’s equation with time-dependent linear potential, see Feng
                    o
(2001).
    As a by-product we solve for a family of Volterra integral equations, which were
previously only treated numerically, see Peskir (2001).
                                          References
 [1] Feng, M. (2001). Complete solution of the Schr¨dinger equation for the time-dependent linear
                                                    o
     potential, Phys. Rev . A 64, 034101.
 [2] Kardaras, K (2007). On the density of first passage times for difussions. on preparation.
 [3] Peskir, G. (2001). On integral equations arising in the first-passage problem for Brownian
     motion, J. Integral Equations Appl., 14.
 [4] Revuz, D., and M. Yor. (2005). Continuous martingales and Brownian motion, Springer-
     Verlag, New York.

Rama Cont, March 11 2008


My talk will be based on Chapter 5 of our book

Financial Modelling with Jump Processes
http://www.cmap.polytechnique.fr/~rama/Jumps/

and the paper by

Kallsen & Tankov
Characterization of dependence of multidimensional Lévy processes using Lévy copulas
Journal of Multivariate Analysis
Volume 97, Issue 7, August 2006, Pages 1551-1572

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