DOCSLIB.ORG

Stein's Method for Discrete Gibbs Measures

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Stein's Method for Discrete Gibbs Measures The Annals of Applied Probability 2008, Vol. 18, No. 4, 1588–1618 DOI: 10.1214/07-AAP0498 c Institute of Mathematical Statistics, 2008 STEIN’S METHOD FOR DISCRETE GIBBS MEASURES1 By Peter Eichelsbacher and Gesine Reinert Ruhr-Universit¨at Bochum and University of Oxford Stein’s method provides a way of bounding the distance of a prob- ability distribution to a target distribution µ. Here we develop Stein’s method for the class of discrete Gibbs measures with a density eV , where V is the energy function. Using size bias couplings, we treat an example of Gibbs convergence for strongly correlated random vari- ables due to Chayes and Klein [Helv. Phys. Acta 67 (1994) 30–42]. We obtain estimates of the approximation to a grand-canonical Gibbs ensemble. As side results, we slightly improve on the Barbour, Holst and Janson [Poisson Approximation (1992)] bounds for Poisson ap- proximation to the sum of independent indicators, and in the case of the geometric distribution we derive better nonuniform Stein bounds than Brown and Xia [Ann. Probab. 29 (2001) 1373–1403]. 0. Introduction. Stein [17] introduced an elegant method for proving convergence of random variables toward a standard normal variable. Bar- bour [2, 3] and G¨otze [10] developed a dynamical point of view of Stein’s method using time-reversible Markov processes. If µ is the stationary dis- tribution of a homogeneous Markov process with generator , then X µ if and only if E g(X) = 0 for all functions g in the domainA ( ) of∼ the operator . ForA any random variable W and for any suitable functionD A f, to assess theA distance Ef(W ) f dµ we first find a solution g of the equation | − | g(Rx)= f(x) f dµ. A − Z If g is in the domain ( ) of , then we obtain D A A arXiv:0808.2877v1 [math.PR] 21 Aug 2008 (0.1) Ef(W ) f dµ = E g(W ) . − | A | Z Received April 2006; revised October 2007. 1Supported in part by the DAAD Foundation and the British Council, ARC Project, Contract 313-ARC-00/27749 and by the RiP program at Oberwolfach, Germany. AMS 2000 subject classifications. Primary 60E05; secondary 60F05, 60E15, 82B05. Key words and phrases. Stein’s method, Gibbs measures, birth and death processes, size bias coupling. This is an electronic reprint of the original article published by the Institute of Mathematical Statistics in The Annals of Applied Probability, 2008, Vol. 18, No. 4, 1588–1618. This reprint differs from the original in pagination and typographic detail. 1 2 P. EICHELSBACHER AND G. REINERT Bounding the right-hand side of (0.1) for a sufficiently large class of func- tions f leads to bounds on distances between the distributions. Here we will mainly focus on the total variation distance, where indicator functions are the test functions to consider. Such distributional bounds are not only useful for limits, but they are also of particular interest, for example, when the distance to the target distri- bution is not negligible. Such relatively large distances occur, for example, when only few observations are available, or when there is considerable de- pendence in the data slowing down the convergence. The bound on the dis- tance can then be taken into account explicitly, for example when deriving confidence intervals. In Section 1 we introduce Stein’s method using the generator approach for discrete Gibbs measures, which are probability measures on N0 with proba- bility weights µ(k) proportional to exp(V (k)) for some function V : N0 R. The discrete Gibbs measures include the classical distributions Poisson,→ bi- nomial, geometric, negative binomial, hypergeometric and the discrete uni- form, to name but a few. One can construct simple birth–death processes, which are time-reversible, and which have a discrete Gibbs measure as its equilibrium measure. In the context of spatial Gibbs measures this connec- tion was introduced by Preston [15]. In Section 2 we not only recall bounds for the increments of the solution of the Stein equation from [5], but we also derive bounds on the solution itself, in terms of potential function of the Gibbs measure; see Lemmas 2.1, 2.4 and 2.5. The bounds, which to our knowledge are new, are illustrated for the Poisson, the binomial and the geometric distribution. For nonnegative random variables, the size bias coupling is a very useful approach to disentangle dependence. Its formulation and its application to assess the distance to Gibbs measures are described in Section 3. We compare the distributions by comparing their respective generators, an idea also used in [11], while paying special attention to the case that the domains of the two generators are not identical. The size bias coupling then naturally leads to Theorem 3.5 and Corollary 3.8. Section 4 applies these theoretical results to assess the distance to a Gibbs distribution for the law of a sum of possibly strongly correlated random variables. The main results are Theorem 4.2 and Proposition 4.4, where we give general bounds for the total variation distance between the distribution of certain sums of strongly correlated random variables and discrete Gibbs distributions of a grand-canonical form; see [16], Section 1.2.3. In particular, Theorem 4.2 gives a bound on the rate of convergence for the qualitative results in [6] by bounding the rate of convergence. Considering two examples with nontrivial interaction, we obtain bounds to limiting nonclassical Gibbs distributions. Our bound on the approxima- tion error is phrased in terms of the particle number and the average density of the particles. STEIN’S METHOD FOR DISCRETE GIBBS MEASURES 3 Summarizing, the main advantage of our considerations is the application of Stein’s method to models with interaction described by Gibbs measures. When applying our bounds on the solution of the Stein equation to the Poisson distribution and the geometric distribution, surprisingly we obtain improved bounds for these well-studied distributions. Thus our investigation of discrete Gibbs measures serves also as a vehicle for obtaining results for classical discrete distributions. The results presented here will also provide a foundation for introducing Stein’s method for spatial Gibbs measures and Gibbs point processes, in forthcoming work. 1. Gibbs measures and birth–death processes. 1.1. Birth–death processes. A birth–death process X(t),t R is a Markov process on the state space 0, 1,...,N , where N N { ,∈ characterized} { } ∈ 0 ∪ {∞} by (nonnegative) birth rates bj, j 0, 1,...,N and (nonnegative) death rates d , j 1,...,N and{ has a∈ generator { }} { j ∈ { }} (1.1) ( h)(j)= b (h(j + 1) h(j)) d (h(j) h(j 1)) A j − − j − − with j 0, 1,...,N . ∈ { } It is well known that for any given probability distribution µ on N0 one can construct a birth–death process which has this distribution as its station- ary distribution. For N = , recurrence of the birth–death process X( ) is equivalent to d1···dn ∞= . The process X( ) is ergodic if and only· if n≥1 b1···bn ∞ · the process is recurrent and c :=1+ b0···bn−1 < . For N < , irre- P n≥1 d1···dn ducibility and hence ergodicity holds if b > 0, j = 0,∞1,...,N 1,∞b = 0 P j − N and dj > 0, j = 1,...,N. In either case the stationary distribution of the ergodic process is given by µ(0) = 1/c and b b (1.2) µ(n)= µ(0) 0 · · · n−1 . d d 1 · · · n For any given probability distribution µ on N0 these recursive formulas give the corresponding class of birth–death processes which have µ as the sta- tionary distribution. For the choice of a unit per capita death rate dj = j one simply obtains that µ(j + 1) (1.3) b = (j + 1), j µ(j) for j N 1. Here and throughout, if N = , then by j N 1 we mean j = 0≤, 1,....− The choice of these rates corresponds∞ to the≤ case− where the detailed balance condition (1.4) µ(j)b = µ(j + 1)d , j = 0, 1,...,N 1, j j+1 − 4 P. EICHELSBACHER AND G. REINERT holds; see, for example, [1] and [12]. We will apply these well-known facts to the discrete Gibbs measure introduced in the next subsection. Now might be a good time to note that not all probability distributions on N0 are given in closed form expressions; notable exceptions occur, for example, in compound Poisson distributions. 1.2. Gibbs measures as stationary distributions of birth–death processes. Gibbs measures can be viewed as stationary measures of birth–death pro- cesses, as follows. We start with a discrete Gibbs measure µ; for convenience we assume that µ has support supp(µ)= 0,...,N , where N N0 , so that µ is given by { } ∈ ∪ {∞} 1 ωk (1.5) µ(k)= eV (k) , k = 0, 1,...,N, k! Z N R N ωk for some function V : 0 . Here = k=0 exp(V (k)) k! , and ω> 0 is fixed. We assume that →exists; isZ known as the partition function in P models of statistical mechanics.Z WeZ set V (k)= for k > N. In terms of statistical mechanics, µ is a grand-canonical ensemble,−∞ ω is the activity and V is the potential energy; see [16], Chapter 1.2. The class of discrete Gibbs measures in (1.5) is equivalent to the class of all discrete probability distributions on N0 by the following simple identifi- cation: For a given probability distribution (µ(k))k∈N0 we have (1.6) V (k) = log µ(k) + log k! + log k log ω, k = 0, 1,...,N, Z− V (0) with V (0) = log µ(0) + log .
Load more

Recommended publications
  • Ruelle Operator for Continuous Potentials and DLR-Gibbs Measures
    Ruelle Operator for Continuous Potentials and DLR-Gibbs Measures Leandro Cioletti Artur O. Lopes Departamento de Matem´atica - UnB Departamento de Matem´atica - UFRGS 70910-900, Bras´ılia, Brazil 91509-900, Porto Alegre, Brazil [email protected] [email protected] Manuel Stadlbauer Departamento de Matem´atica - UFRJ 21941-909, Rio de Janeiro, Brazil [email protected] October 25, 2019 Abstract In this work we study the Ruelle Operator associated to a continuous potential defined on a countable product of a compact metric space. We prove a generaliza- tion of Bowen’s criterion for the uniqueness of the eigenmeasures. One of the main results of the article is to show that a probability is DLR-Gibbs (associated to a continuous translation invariant specification), if and only if, is an eigenprobability for the transpose of the Ruelle operator. Bounded extensions of the Ruelle operator to the Lebesgue space of integrable functions, with respect to the eigenmeasures, are studied and the problem of exis- tence of maximal positive eigenfunctions for them is considered. One of our main results in this direction is the existence of such positive eigenfunctions for Bowen’s potential in the setting of a compact and metric alphabet. We also present a version of Dobrushin’s Theorem in the setting of Thermodynamic Formalism. Keywords: Thermodynamic Formalism, Ruelle operator, continuous potentials, Eigenfunc- tions, Equilibrium states, DLR-Gibbs Measures, uncountable alphabet. arXiv:1608.03881v6 [math.DS] 24 Oct 2019 MSC2010: 37D35, 28Dxx, 37C30. 1 Introduction The classical Ruelle operator needs no introduction and nowadays is a key concept of Thermodynamic Formalism.
    [Show full text]
  • The Dobrushin Comparison Theorem Is a Powerful Tool to Bound The
    COMPARISON THEOREMS FOR GIBBS MEASURES∗ BY PATRICK REBESCHINI AND RAMON VAN HANDEL Princeton University The Dobrushin comparison theorem is a powerful tool to bound the dif- ference between the marginals of high-dimensional probability distributions in terms of their local specifications. Originally introduced to prove unique- ness and decay of correlations of Gibbs measures, it has been widely used in statistical mechanics as well as in the analysis of algorithms on random fields and interacting Markov chains. However, the classical comparison theorem requires validity of the Dobrushin uniqueness criterion, essentially restricting its applicability in most models to a small subset of the natural parameter space. In this paper we develop generalized Dobrushin comparison theorems in terms of influences between blocks of sites, in the spirit of Dobrushin- Shlosman and Weitz, that substantially extend the range of applicability of the classical comparison theorem. Our proofs are based on the analysis of an associated family of Markov chains. We develop in detail an application of our main results to the analysis of sequential Monte Carlo algorithms for filtering in high dimension. CONTENTS 1 Introduction . .2 2 Main Results . .4 2.1 Setting and notation . .4 2.2 Main result . .6 2.3 The classical comparison theorem . .8 2.4 Alternative assumptions . .9 2.5 A one-sided comparison theorem . 11 3 Proofs . 12 3.1 General comparison principle . 13 3.2 Gibbs samplers . 14 3.3 Proof of Theorem 2.4................................. 18 3.4 Proof of Corollary 2.8................................. 23 3.5 Proof of Theorem 2.12................................ 25 4 Application: Particle Filters .
    [Show full text]
  • Continuum Percolation for Gibbs Point Processes Dominated by a Poisson Process, and Then We Use the Percolation Results Available for Poisson Processes
    Electron. Commun. Probab. 18 (2013), no. 67, 1–10. ELECTRONIC DOI: 10.1214/ECP.v18-2837 COMMUNICATIONS ISSN: 1083-589X in PROBABILITY Continuum percolation for Gibbs point processes∗ Kaspar Stuckiy Abstract We consider percolation properties of the Boolean model generated by a Gibbs point process and balls with deterministic radius. We show that for a large class of Gibbs point processes there exists a critical activity, such that percolation occurs a.s. above criticality. Keywords: Gibbs point process; Percolation; Boolean model; Conditional intensity. AMS MSC 2010: 60G55; 60K35. Submitted to ECP on May 29, 2013, final version accepted on August 1, 2013. Supersedes arXiv:1305.0492v1. 1 Introduction Let Ξ be a point process in Rd, d ≥ 2, and fix a R > 0. Consider the random S B B set ZR(Ξ) = x2Ξ R(x), where R(x) denotes the open ball with radius R around x. Each connected component of ZR(Ξ) is called a cluster. We say that Ξ percolates (or R-percolates) if ZR(Ξ) contains with positive probability an infinite cluster. In the terminology of [9] this is a Boolean percolation model driven by Ξ and deterministic radius distribution R. It is well-known that for Poisson processes there exists a critical intensity βc such that a Poisson processes with intensity β > βc percolates a.s. and if β < βc there is a.s. no percolation, see e.g. [17], or [14] for a more general Poisson percolation model. For Gibbs point processes the situation is less clear. In [13] it is shown that for some | downloaded: 28.9.2021 two-dimensional pairwise interacting models with an attractive tail, percolation occurs if the activity parameter is large enough, see also [1] for a similar result concerning the Strauss hard core process in two dimensions.
    [Show full text]
  • Mean-Field Monomer-Dimer Models. a Review. 3
    Mean-Field Monomer-Dimer models. A review. Diego Alberici, Pierluigi Contucci, Emanuele Mingione University of Bologna - Department of Mathematics Piazza di Porta San Donato 5, Bologna (Italy) E-mail: [email protected], [email protected], [email protected] To Chuck Newman, on his 70th birthday Abstract: A collection of rigorous results for a class of mean-field monomer- dimer models is presented. It includes a Gaussian representation for the partition function that is shown to considerably simplify the proofs. The solutions of the quenched diluted case and the random monomer case are explained. The presence of the attractive component of the Van der Waals potential is considered and the coexistence phase coexistence transition analysed. In particular the breakdown of the central limit theorem is illustrated at the critical point where a non Gaussian, quartic exponential distribution is found for the number of monomers centered and rescaled with the volume to the power 3/4. 1. Introduction The monomer-dimer models, an instance in the wide set of interacting particle systems, have a relevant role in equilibrium statistical mechanics. They were introduced to describe, in a simplified yet effective way, the process of absorption of monoatomic or diatomic molecules in condensed-matter physics [15,16,39] or the behaviour of liquid solutions composed by molecules of different sizes [25]. Exact solutions in specific cases (e.g. the perfect matching problem) have been obtained on planar lattices [24,26,34,36,41] and the problem on regular lattices is arXiv:1612.09181v1 [math-ph] 29 Dec 2016 also interesting for the liquid crystals modelling [1,20,27,31,37].
    [Show full text]
  • Markov Random Fields and Gibbs Measures
    Markov Random Fields and Gibbs Measures Oskar Sandberg December 15, 2004 1 Introduction A Markov random field is a name given to a natural generalization of the well known concept of a Markov chain. It arrises by looking at the chain itself as a very simple graph, and ignoring the directionality implied by “time”. A Markov chain can then be seen as a chain graph of stochastic variables, where each variable has the property that it is independent of all the others (the future and past) given its two neighbors. With this view of a Markov chain in mind, a Markov random field is the same thing, only that rather than a chain graph, we allow any graph structure to define the relationship between the variables. So we define a set of stochastic variables, such that each is independent of all the others given its neighbors in a graph. Markov random fields can be defined both for discrete and more complicated valued random variables. They can also be defined for continuous index sets, in which case more complicated neighboring relationships take the place of the graph. In what follows, however, we will look only at the most appro- achable cases with discrete index sets, and random variables with finite state spaces (for the most part, the state space will simply be {0, 1}). For a more general treatment see for instance [Rozanov]. 2 Definitions 2.1 Markov Random Fields Let X1, ...Xn be random variables taking values in some finite set S, and let G = (N, E) be a finite graph such that N = {1, ..., n}, whose elements will 1 sometime be called sites.
    [Show full text]
  • A Note on Gibbs and Markov Random Fields with Constraints and Their Moments
    NISSUNA UMANA INVESTIGAZIONE SI PUO DIMANDARE VERA SCIENZIA S’ESSA NON PASSA PER LE MATEMATICHE DIMOSTRAZIONI LEONARDO DA VINCI vol. 4 no. 3-4 2016 Mathematics and Mechanics of Complex Systems ALBERTO GANDOLFI AND PIETRO LENARDA A NOTE ON GIBBS AND MARKOV RANDOM FIELDS WITH CONSTRAINTS AND THEIR MOMENTS msp MATHEMATICS AND MECHANICS OF COMPLEX SYSTEMS Vol. 4, No. 3-4, 2016 dx.doi.org/10.2140/memocs.2016.4.407 ∩ MM A NOTE ON GIBBS AND MARKOV RANDOM FIELDS WITH CONSTRAINTS AND THEIR MOMENTS ALBERTO GANDOLFI AND PIETRO LENARDA This paper focuses on the relation between Gibbs and Markov random fields, one instance of the close relation between abstract and applied mathematics so often stressed by Lucio Russo in his scientific work. We start by proving a more explicit version, based on spin products, of the Hammersley–Clifford theorem, a classic result which identifies Gibbs and Markov fields under finite energy. Then we argue that the celebrated counterexample of Moussouris, intended to show that there is no complete coincidence between Markov and Gibbs random fields in the presence of hard-core constraints, is not really such. In fact, the notion of a constrained Gibbs random field used in the example and in the subsequent literature makes the unnatural assumption that the constraints are infinite energy Gibbs interactions on the same graph. Here we consider the more natural extended version of the equivalence problem, in which constraints are more generally based on a possibly larger graph, and solve it. The bearing of the more natural approach is shown by considering identifi- ability of discrete random fields from support, conditional independencies and corresponding moments.
    [Show full text]
  • Large Deviations and Applications 29. 11. Bis 5. 12. 1992
    MATHEMATISCHES FORSCHUNGSINSTITUT OBERWOLFACH Tagungsbericht 51/1992 Large Deviations and Applications 29. 11. bis 5. 12. 1992 Zu dieser Tagung unter der gemeinsamen Leitung von E. Bolthausen (Zurich), J. Gartner (Berlin) und S. R. S. Varadhan (New York) trafen sich Mathematiker und mathematische Physiker aus den verschieden- sten Landern mit einem breiten Spektrum von Interessen. Die Theorie vom asymptotischen Verhalten der Wahrscheinlichkeiten groer Abweichungen ist einer der Schwerpunkte der jungeren wahr- scheinlichkeitstheoretischen Forschung. Es handelt sich um eine Pra- zisierung von Gesetzen groer Zahlen. Gegenstand der Untersuchun- gen sind sowohl die Skala als auch die Rate des exponentiellen Abfalls der kleinen Wahrscheinlichkeiten, mit denen ein untypisches Verhal- ten eines stochastischen Prozesses auftritt. Die Untersuchung dieser kleinen Wahrscheinlichkeiten ist fur viele Fragestellungen interessant. Wichtige Themen der Tagung waren: – Anwendungen groer Abweichungen in der Statistik – Verschiedene Zugange zur Theorie groer Abweichungen – Stochastische Prozesse in zufalligen Medien – Wechselwirkende Teilchensysteme und ihre Dynamik – Statistische Mechanik, Thermodynamik – Hydrodynamischer Grenzubergang – Verhalten von Grenzachen, monomolekulare Schichten – Dynamische Systeme und zufallige Storungen – Langreichweitige Wechselwirkung, Polymere – Stochastische Netzwerke Die Tagung hatte 47 Teilnehmer, es wurden 42 Vortrage gehalten. 1 Abstracts G. Ben Arous (joint work with A. Guionnet) Langevin dynamics for spin glasses We study the dynamics for the Sherrington-Kirkpatrick model of spin glasses. More precisely, we take a soft spin model suggested by Som- polinski and Zippelius. Thus we consider diusions interacting with a Gaussian random potential of S-K-type. (1) We give an “annealed” large deviation principle for the empirical measure at the process level. We deduce from this an annealed law of large numbers and a central limit theorem.
    [Show full text]
  • Boundary Conditions for Translation- Invariant Gibbs Measures of the Potts Model on Cayley Trees Daniel Gandolfo, M
    Boundary Conditions for Translation- Invariant Gibbs Measures of the Potts Model on Cayley Trees Daniel Gandolfo, M. M. Rakhmatullaev, U.A. Rozikov To cite this version: Daniel Gandolfo, M. M. Rakhmatullaev, U.A. Rozikov. Boundary Conditions for Translation- In- variant Gibbs Measures of the Potts Model on Cayley Trees. Journal of Statistical Physics, Springer Verlag, 2017, 167 (5), pp.1164. 10.1007/s10955-017-1771-5. hal-01591298 HAL Id: hal-01591298 https://hal.archives-ouvertes.fr/hal-01591298 Submitted on 26 Apr 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Boundary conditions for translation-invariant Gibbs measures of the Potts model on Cayley trees D. Gandolfo Aix Marseille Univ, Universit´ede Toulon, CNRS, CPT, Marseille, France. [email protected] M. M. Rahmatullaev and U. A. Rozikov Institute of mathematics, 29, Do'rmon Yo'li str., 100125, Tashkent, Uzbekistan. [email protected] [email protected] Abstract We consider translation-invariant splitting Gibbs measures (TISGMs) for the q- state Potts model on a Cayley tree of order two. Recently a full description of the TISGMs was obtained, and it was shown in particular that at sufficiently low temper- atures their number is 2q − 1.
    [Show full text]
  • Arxiv:2103.07391V1 [Math.PR] 12 Mar 2021 Studied
    GIBBS MEASURES OF POTTS MODEL ON CAYLEY TREES: A SURVEY AND APPLICATIONS U.A. ROZIKOV Abstract. In this paper we give a systematic review of the theory of Gibbs mea- sures of Potts model on Cayley trees (developed since 2013) and discuss many applications of the Potts model to real world situations: mainly biology, physics, and some examples of alloy behavior, cell sorting, financial engineering, flocking birds, flowing foams, image segmentation, medicine, sociology etc. 1. Introduction The Potts model is defined by a Hamiltonian (energy) of configurations of spins which take one of q possible values on vertices of a lattice. The model is used to study the behavior of systems having multiple states (spins, colors, alleles, etc). Since the model has a rich mathematical formulation it has been studied extensively. Usually the results of investigations of a system (in particular, the Potts model) are presented by a measure assigning a number to each (interesting) suitable properties of the system. The Gibbs measure is one of such important measures in many problems of probability theory and statistical mechanics. It is the measure associated with the Hamiltonian of a (biological, physical) system. Each Gibbs measure gives a state of the system. The main problem for a given Hamiltonian on a countable lattice is to describe its all possible Gibbs measures. In case of uniqueness of the Gibbs measure (for all values of the parameters, in particular a parameter can be temperature), the system does not change its state. The existence of some values of parameters at which the uniqueness of Gibbs measure switches to non-uniqueness is interpreted as phase transition (the system will change its state).
    [Show full text]
  • Lecture Notes Gibbs Measures and Phase Transitions. Part 2
    Lecture notes Gibbs measures and phase transitions. Part 2 Anton Bovier Rheinische Friedrich-Wilhelms Universit¨at Bonn Institut f¨ur Angewandte Mathematik Endenicher Allee 60 53115 Bonn Contents 1 Gibbsian formalism and metastates page 1 1.1 Introduction 1 1.2 Random Gibbs measures and metastates 3 1.3 Remarks on uniqueness conditions 13 1.4 Phase transitions 13 1.5 The Edwards–Anderson model. 17 2 The random-field Ising model 19 2.1 The Imry–Ma argument 19 2.2 The Aizenman–Wehr method 28 2.2.1 Translation covariant states 28 2.2.2 Order parameters and generating functions 30 2.3 The Bricmont-Kupiainen renormalization group 36 2.3.1 Renormalization group and contour models 38 2.3.2 The ground-states 44 2.3.3 The Gibbs states at finite temperature 58 Bibliography 77 i 1 Gibbsian formalism and metastates Longtemps les objets dont s’occupent les math´ematiciens ´etaient pour la pluspart mal d´efinis; on croyait les connaˆıtre, parce qu’on se les repr´esentait avec le sens ou l’imagination; mais on n’en avait qu’une image grossi`ere et non une id´ee pr´ecise sur laquelle le raisonnement pˆut avoir prise1. Henri Poincar´e, La valeur de la science We now turn to the main topic of this book, disordered systems. We split this into two parts, treating in turn lattice models and mean-field models. From the physical point of view, the former should be more realistic and hence more relevant, so it is natural that we present the general mathemat- ical framework in this context.
    [Show full text]
  • Distribution Dependent Stochastic Differential Equations
    Distribution Dependent Stochastic Differential Equations∗ Xing Huanga), Panpan Renb), Feng-Yu Wanga) a) Center for Applied Mathematics, Tianjin University, Tianjin 300072, China b) Department of Mathematics, City University of HongKong, HongKong, China [email protected], [email protected], [email protected] December 29, 2020 Abstract Due to their intrinsic link with nonlinear Fokker-Planck equations and many other applications, distribution dependent stochastic differential equations (DDSDEs for short) have been intensively investigated. In this paper we summarize some recent progresses in the study of DDSDEs, which include the correspondence of weak solutions and nonlinear Fokker-Planck equations, the well-posedness, regularity estimates, exponential ergodicity, long time large deviations, and comparison theorems. AMS subject Classification: 60B05, 60B10. Keywords: DDSDE, nonlinear Fokker-Planck equation, Bismut formula, Wasserstein distance, gradient estimate. arXiv:2012.13656v1 [math.PR] 26 Dec 2020 1 Introduction To characterize nonlinear PDEs in Vlasov’s kinetic theory, Kac [27, 28] proposed the “propagation of chaos” of mean field particle systems, which stimulated McKean [33] to study nonlinear Fokker-Planck equations using stochastic differential equations with distribution dependent drifts, see [45] for a theory on mean field particle systems and applications. In general, a nonlinear Fokker-Planck equation can be characterized by the following distri- bution dependent stochastic differential equations (DDSDEs for short): L L (1.1) dXt = b(t, Xt, Xt )dt + σ(t, Xt, Xt )dWt, ∗Supported in part by NNSFC (11771326, 11831014, 11921001, 11801406). 1 where Wt is an m-dimensional Brownian motion on a complete filtration probability space (Ω, F , P), L is the distribution (i.e.
    [Show full text]
  • The Random Geometry of Equilibrium Phases
    The random geometry of equilibrium phases Hans-Otto Georgii, Mathematisches Institut, Ludwig-Maximilians-Universit¨at, D-80333 M¨unchen, Germany. Email: [email protected] Olle H¨aggstr¨om, Department of Mathematics, Chalmers University of Technology, S-412 96 G¨oteborg, Sweden. Email: [email protected] Christian Maes, Instituut voor Theoretische Fysica, K.U.Leuven, B-3001 Leuven, Belgium. Email: [email protected] Contents 1 Introduction 3 2 Equilibrium phases 6 2.1 Thelattice .................................. 6 2.2 Configurations ................................ 6 2.3 Observables.................................. 7 2.4 Randomfields................................. 8 2.5 TheHamiltonian ............................... 8 2.6 Gibbsmeasures................................ 9 2.7 Phasetransitionandphases . 11 arXiv:math/9905031v1 [math.PR] 5 May 1999 3 Some models 14 3.1 TheferromagneticIsingmodel . 14 3.2 TheantiferromagneticIsingmodel . 15 3.3 ThePottsmodel ............................... 16 3.4 Thehard-corelatticegasmodel. 17 3.5 The Widom–Rowlinson lattice model . 18 4 Coupling and stochastic domination 19 4.1 Thecouplinginequality . 19 4.2 Stochasticdomination . 20 4.3 ApplicationstotheIsingmodel . 23 4.4 Applicationtoothermodels . 26 1 5 Percolation 28 5.1 Bernoullipercolation . 28 5.2 Dependent percolation: the role of the density . ....... 32 5.3 Examples of dependent percolation . 34 5.4 Thenumberofinfiniteclusters . 37 6 Random-cluster representations 41 6.1 Random-clusterandPottsmodels . 41 6.2 Infinite-volumelimits. 44 6.3 PhasetransitioninthePottsmodel . 47 6.4 Infinite volume random-cluster measures . ..... 50 6.5 An application to percolation in the Ising model . ...... 55 6.6 Cluster algorithms for computer simulation . ...... 56 6.7 Random-cluster representation of the Widom–Rowlinson model ..... 58 7 Uniqueness and exponential mixing from non-percolation 61 7.1 Disagreementpaths.............................
    [Show full text]