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Hierarchical Dirichlet processes
 Journal of the American Statistical Association
, 2004
"... program. The authors wish to acknowledge helpful discussions with Lancelot James and Jim Pitman and the referees for useful comments. 1 We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture comp ..."
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Cited by 927 (79 self)
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program. The authors wish to acknowledge helpful discussions with Lancelot James and Jim Pitman and the referees for useful comments. 1 We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture
The twoparameter PoissonDirichlet distribution derived from a stable subordinator.
, 1995
"... The twoparameter PoissonDirichlet distribution, denoted pd(ff; `), is a distribution on the set of decreasing positive sequences with sum 1. The usual PoissonDirichlet distribution with a single parameter `, introduced by Kingman, is pd(0; `). Known properties of pd(0; `), including the Markov ..."
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Cited by 364 (33 self)
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The twoparameter PoissonDirichlet distribution, denoted pd(ff; `), is a distribution on the set of decreasing positive sequences with sum 1. The usual PoissonDirichlet distribution with a single parameter `, introduced by Kingman, is pd(0; `). Known properties of pd(0; `), including the Markov chain description due to VershikShmidtIgnatov, are generalized to the twoparameter case. The sizebiased random permutation of pd(ff; `) is a simple residual allocation model proposed by Engen in the context of species diversity, and rediscovered by Perman and the authors in the study of excursions of Brownian motion and Bessel processes. For 0 ! ff ! 1, pd(ff; 0) is the asymptotic distribution of ranked lengths of excursions of a Markov chain away from a state whose recurrence time distribution is in the domain of attraction of a stable law of index ff. Formulae in this case trace back to work of Darling, Lamperti and Wendel in the 1950's and 60's. The distribution of ranked lengths of e...
Infinite Latent Feature Models and the Indian Buffet Process
, 2005
"... We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution ..."
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Cited by 274 (46 self)
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We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution
From Pitman’s theorem to crystals
, 2013
"... Abstract. We describe an extension of Pitman’s theorem on Brownian motion and the three dimensional Bessel process to several dimensions. We show how this extension is suggested by considering a random walk on a noncommutative space, and is connected with crystals and Littelmann paths. Two of the mo ..."
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Abstract. We describe an extension of Pitman’s theorem on Brownian motion and the three dimensional Bessel process to several dimensions. We show how this extension is suggested by considering a random walk on a noncommutative space, and is connected with crystals and Littelmann paths. Two
Revised 5 report on the algorithmic language Scheme
 ACM SIGPLAN Notices
, 1998
"... The report gives a defining description of the programming language Scheme. Scheme is a statically scoped and properly tailrecursive dialect of the Lisp programming language invented by Guy Lewis Steele Jr. and Gerald Jay Sussman. It was designed to have an exceptionally clear and simple semantics ..."
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Cited by 244 (4 self)
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The report gives a defining description of the programming language Scheme. Scheme is a statically scoped and properly tailrecursive dialect of the Lisp programming language invented by Guy Lewis Steele Jr. and Gerald Jay Sussman. It was designed to have an exceptionally clear and simple semantics and few different ways to form expressions. A wide variety of programming paradigms, including imperative, functional, and message passing styles, find convenient expression in Scheme. The introduction offers a brief history of the language and of the report. The first three chapters present the fundamental ideas of the language and describe the notational conventions used for describing the language and for writing programs in the language.
Combinatorial stochastic processes
"... This is a collection of expository articles about various topics at the interface between enumerative combinatorics and stochastic processes. These articles expand on a course of lectures given at the École d’Été de Probabilités de St. Flour in July 2002. The articles are called ’chapters ’ and numb ..."
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Cited by 219 (15 self)
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This is a collection of expository articles about various topics at the interface between enumerative combinatorics and stochastic processes. These articles expand on a course of lectures given at the École d’Été de Probabilités de St. Flour in July 2002. The articles are called ’chapters ’ and numbered according to the order of these chapters in a printed volume to appear in Springer Lecture Notes in Mathematics. Each chapter is fairly selfcontained, so readers with adequate background can start reading any chapter, with occasional consultation of earlier chapters as necessary. Following this Chapter 0, there are 10 chapters, each divided into sections. Most sections conclude with some Exercises. Those for which I don’t know solutions are called Problems. Acknowledgments Much of the research reviewed here was done jointly with David Aldous. Much credit is due to him, especially for the big picture of continuum approximations to large combinatorial structures. Thanks also to my other collaborators in this work, especially Jean Bertoin, Michael Camarri, Steven
Deterministic and Stochastic Models for Coalescence (Aggregation, Coagulation): a Review of the MeanField Theory for Probabilists
 Bernoulli
, 1997
"... Consider N particles, which merge into clusters according to the rule: a cluster of size x and a cluster of size y merge at (stochastic) rate K(x; y)=N , where K is a specified rate kernel. This MarcusLushnikov model of stochastic coalescence, and the underlying deterministic approximation given by ..."
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Cited by 227 (13 self)
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Consider N particles, which merge into clusters according to the rule: a cluster of size x and a cluster of size y merge at (stochastic) rate K(x; y)=N , where K is a specified rate kernel. This MarcusLushnikov model of stochastic coalescence, and the underlying deterministic approximation given by the Smoluchowski coagulation equations, have an extensive scientific literature. Some mathematical literature (Kingman's coalescent in population genetics; component sizes in random graphs) implicitly studies the special cases K(x; y) = 1 and K(x; y) = xy. We attempt a wideranging survey. General kernels are only now starting to be studied rigorously, so many interesting open problems appear. Keywords. branching process, coalescence, continuum tree, densitydependent Markov process, gelation, random graph, random tree, Smoluchowski coagulation equation Research supported by N.S.F. Grant DMS9622859 1 Introduction Models, implicitly or explicitly stochastic, of coalescence (= coagulati...
Limits to Parallel Computation: PCompleteness Theory
, 1995
"... D. Kavadias, L. M. Kirousis, and P. G. Spirakis. The complexity of the reliable connectivity problem. Information Processing Letters, 39(5):245252, 13 September 1991. (135) [206] P. Kelsen. On computing a maximal independent set in a hypergraph of constant dimension in parallel. In Proceedings of ..."
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Cited by 167 (5 self)
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D. Kavadias, L. M. Kirousis, and P. G. Spirakis. The complexity of the reliable connectivity problem. Information Processing Letters, 39(5):245252, 13 September 1991. (135) [206] P. Kelsen. On computing a maximal independent set in a hypergraph of constant dimension in parallel. In Proceedings of the TwentyFourth Annual ACM Symposium on Theory of Computing, pages 339369, Victoria, B.C., Canada, May 1992. (225) [207] L. G. Khachian. A polynomial time algorithm for linear programming. Doklady Akademii Nauk SSSR, n.s., 244(5):10931096, 1979. English translation in Soviet Math. Dokl. 20, 191194. (150, 151, 153) [208] S. Khuller. On computing graph closures. Information Processing Letters, 31(5):249255, 12 June 1989. (142, 224) [209] S. Khuller and B. Schieber. E#cient parallel algorithms for testing k connectivity and finding disjoint st paths in graphs. SIAM Journal on Computing, 20(2):352375, April 1991. (134) [210] G. A. P. Kindervater and J. K. Lenstra. An introduction to parallelism in combinatorial optimization. In J. van Leeuwen and J. K. Lenstra, editors, Parallel Computers and Computation, volume 9 of CWI Syllabus, pages 163184. Center for Mathematics and Computer Science, Amsterdam, The Netherlands, 1985. (17) [211] G. A. P. Kindervater and J. K. Lenstra. Parallel algorithms. In M. O'hEigeartaigh, J. K. Lenstra, and A. H. G. Rinnooy Kan, editors, Combinatorial Optimization: Annotated Bibliographies, chapter 8, pages 106128. John Wiley & Sons, Chichester, 1985. (17, 21) [212] G. A. P. Kindervater, J. K. Lenstra, and D. B. Shmoys. The parallel complexity of TSP heuristics. Journal of Algorithms, 10(2):249270, June 1989. (138) 272 BIBLIOGRAPHY [213] G. A. P. Kindervater and H. W. J. M. Trienekens. Experiments with parallel algorit...
Results 1  10
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