A large deviations heuristic made precise

2000 ◽  
Vol 128 (3) ◽  
pp. 561-569 ◽  
Author(s):  
NEIL O'CONNELL
Keyword(s):  
Large Deviations ◽  
Bin Packing ◽  
Symmetric Functions ◽  
Large Deviation ◽  
Random Variables ◽  
Packing Problem ◽  
Rate Function ◽  
Empirical Measures ◽  
Underlying Distribution ◽  
Uniformly Lipschitz

Sanov's Theorem states that the sequence of empirical measures associated with a sequence of i.d.d. random variables satisfies the large deviation principle (LDP) in the weak topology with rate function given by a relative entropy. We present a derivative which allows one to establish LDPs for symmetric functions of many i.d.d. random variables under the condition that (i) a law of large numbers holds whatever the underlying distribution and (ii) the functions are uniformly Lipschitz. The heuristic (of the title) is that the LDP follows from (i) provided the functions are ‘sufficiently smooth’. As an application, we obtain large deviations results for the stochastic bin-packing problem.

scholarly journals Large Deviation Analysis of a Droplet Model Having a Poisson Equilibrium Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Richard S. Ellis ◽  
Shlomo Ta’asan
Keyword(s):  
Equilibrium Distribution ◽  
Large Deviation ◽  
Random Variables ◽  
Rate Function ◽  
Number Density ◽  
Droplet Model ◽  
Empirical Measures ◽  
Deviation Analysis ◽  
Droplet Sizes ◽  
Positive Integers

In this paper we use large deviation theory to determine the equilibrium distribution of a basic droplet model that underlies a number of important models in material science and statistical mechanics. Given b∈N and c>b, K distinguishable particles are placed, each with equal probability 1/N, onto the N sites of a lattice, where K/N equals c. We focus on configurations for which each site is occupied by a minimum of b particles. The main result is the large deviation principle (LDP), in the limit K→∞ and N→∞ with K/N=c, for a sequence of random, number-density measures, which are the empirical measures of dependent random variables that count the droplet sizes. The rate function in the LDP is the relative entropy R(θ∣ρ∗), where θ is a possible asymptotic configuration of the number-density measures and ρ∗ is a Poisson distribution with mean c, restricted to the set of positive integers n satisfying n≥b. This LDP implies that ρ∗ is the equilibrium distribution of the number-density measures, which in turn implies that ρ∗ is the equilibrium distribution of the random variables that count the droplet sizes.

A LARGE DEVIATION FOR OCCUPATION TIME OF SUPER α-STABLE PROCESS

2008 ◽  
Vol 11 (01) ◽  
pp. 53-71 ◽  
Author(s):  
QIU-YUE LI ◽  
YAN-XIA REN
Keyword(s):  
Brownian Motion ◽  
Large Deviations ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Stable Process ◽  
Rate Function ◽  
Occupation Time ◽  
Occupation Times ◽  
Tail Probabilities ◽  
Super Brownian Motion

We derive a large deviation principle for occupation time of super α-stable process in ℝd with d > 2α. The decay of tail probabilities is shown to be exponential and the rate function is characterized. Our result can be considered as a counterpart of Lee's work on large deviations for occupation times of super-Brownian motion in ℝd for dimension d > 4 (see Ref. 10).

scholarly journals How to estimate the rate function of a cumulative process

2005 ◽  
Vol 42 (4) ◽  
pp. 1044-1052 ◽  
Author(s):  
Ken Duffy ◽  
Anthony P. Metcalfe
Keyword(s):  
Renewal Process ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Direct Method ◽  
Random Variables ◽  
Rate Function ◽  
Dirac Measure ◽  
Mixing Conditions ◽  
A Value ◽  
Cumulative Process

Consider two sequences of bounded random variables, a value and a timing process, that satisfy the large deviation principle (LDP) with rate function J(⋅,⋅) and whose cumulative process satisfies the LDP with rate function I(⋅). Under mixing conditions, an LDP for estimates of I constructed by transforming an estimate of J is proved. For the case of a cumulative renewal process it is demonstrated that this approach is favourable to a more direct method, as it ensures that the laws of the estimates converge weakly to a Dirac measure at I.

scholarly journals Sample Path Large Deviations of Poisson Shot Noise with Heavy-Tailed Semiexponential Distributions

2011 ◽  
Vol 48 (03) ◽  
pp. 688-698 ◽  
Author(s):  
Ken R. Duffy ◽  
Giovanni Luca Torrisi
Keyword(s):  
Large Deviations ◽  
Shot Noise ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Sample Path ◽  
Rate Function ◽  
Sample Paths ◽  
Heavy Tailed ◽  
Poisson Shot Noise ◽  
Sample Path Large Deviations

It is shown that the sample paths of Poisson shot noise with heavy-tailed semiexponential distributions satisfy a large deviation principle with a rate function that is insensitive to the shot shape. This demonstrates that, on the scale of large deviations, paths to rare events do not depend on the shot shape.

scholarly journals Large deviations for exchangeable observations with applications

2004 ◽  
Vol 2004 (55) ◽  
pp. 2947-2958
Author(s):  
Jinwen Chen
Keyword(s):  
Large Deviations ◽  
Large Deviation ◽  
Random Variables ◽  
Moment Conditions

We first prove some large deviation results for a mixture of i.i.d. random variables. Compared with most of the known results in the literature, our results are built on relaxing some restrictive conditions that may not be easy to be checked in certain typical cases. The main feature in our main results is that we require little knowledge of (continuity of) the component measures and/or of the compactness of the support of the mixing measure. Instead, we pose certain moment conditions, which may be more practical in applications. We then use the large deviation approach to study the problem of estimating the component and the mixing measures.

Monte Carlo simulation and large deviations theory for uniformly recurrent Markov chains

1990 ◽  
Vol 27 (1) ◽  
pp. 44-59 ◽  
Author(s):  
James A Bucklew ◽  
Peter Ney ◽  
John S. Sadowsky
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Large Deviations ◽  
Large Deviation ◽  
Homogeneous Markov Chain ◽  
Underlying Distribution ◽  
Monte Carlo Simulation Technique ◽  
Large Deviations Theory

Importance sampling is a Monte Carlo simulation technique in which the simulation distribution is different from the true underlying distribution. In order to obtain an unbiased Monte Carlo estimate of the desired parameter, simulated events are weighted to reflect their true relative frequency. In this paper, we consider the estimation via simulation of certain large deviations probabilities for time-homogeneous Markov chains. We first demonstrate that when the simulation distribution is also a homogeneous Markov chain, the estimator variance will vanish exponentially as the sample size n tends to∞. We then prove that the estimator variance is asymptotically minimized by the same exponentially twisted Markov chain which arises in large deviation theory, and furthermore, this optimization is unique among uniformly recurrent homogeneous Markov chain simulation distributions.

scholarly journals Extended Laplace principle for empirical measures of a Markov chain

2019 ◽  
Vol 51 (01) ◽  
pp. 136-167 ◽  
Author(s):  
Stephan Eckstein
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Large Deviations ◽  
Large Deviation ◽  
Dual Pair ◽  
Wasserstein Distance ◽  
Large Deviations Principle ◽  
Empirical Measures ◽  
Leibler Divergence ◽  
Laplace Principle

AbstractWe consider discrete-time Markov chains with Polish state space. The large deviations principle for empirical measures of a Markov chain can equivalently be stated in Laplace principle form, which builds on the convex dual pair of relative entropy (or Kullback– Leibler divergence) and cumulant generating functional f ↦ ln ʃ exp (f). Following the approach by Lacker (2016) in the independent and identically distributed case, we generalize the Laplace principle to a greater class of convex dual pairs. We present in depth one application arising from this extension, which includes large deviation results and a weak law of large numbers for certain robust Markov chains—similar to Markov set chains—where we model robustness via the first Wasserstein distance. The setting and proof of the extended Laplace principle are based on the weak convergence approach to large deviations by Dupuis and Ellis (2011).

scholarly journals Multipopulation Spin Models: A View from Large Deviations Theoretic Window

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Alex Akwasi Opoku ◽  
Godwin Osabutey
Keyword(s):  
Large Deviations ◽  
Probability Distributions ◽  
Mean Field ◽  
Variational Formula ◽  
Rate Function ◽  
Large Deviations Principle ◽  
Spin Models ◽  
Empirical Measures ◽  
Rate Functions ◽  
Empirical Means

This paper studies large deviations properties of vectors of empirical means and measures generated as follows. Consider a sequence X1,X2,…,Xn of independent and identically distributed random variables partitioned into d-subgroups with sizes n1,…,nd. Further, consider a d-dimensional vector mn whose coordinates are made up of the empirical means of the subgroups. We prove the following. (1) The sequence of vector of empirical means mn satisfies large deviations principle with rate n and rate function I, when the sequence X1,X2,…,Xn is Rl valued, with l≥1. (2) Similar large deviations results hold for the corresponding sequence of vector of empirical measures Ln if Xi’s, i=1,2,…,n, take on finitely many values. (3) The rate functions for the above large deviations principles are convex combinations of the corresponding rate functions arising from the large deviations principles of the coordinates of mn and Ln. The probability distributions used in the convex combinations are given by α=(α1,…,αd)=limn→∞1/n(n1,…,nd). These results are consequently used to derive variational formula for the thermodynamic limit for the pressure of multipopulation Curie-Weiss (I. Gallo and P. Contucci (2008), and I. Gallo (2009)) and mean-field Pott’s models, via a version of Varadhan’s integral lemma for an equicontinuous family of functions. These multipopulation models serve as a paradigm for decision-making context where social interaction and other socioeconomic attributes of individuals play a crucial role.

scholarly journals How to estimate the rate function of a cumulative process

2005 ◽  
Vol 42 (04) ◽  
pp. 1044-1052 ◽  
Author(s):  
Ken Duffy ◽  
Anthony P. Metcalfe
Keyword(s):  
Renewal Process ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Direct Method ◽  
Random Variables ◽  
Rate Function ◽  
Dirac Measure ◽  
Mixing Conditions ◽  
A Value ◽  
Cumulative Process

Consider two sequences of bounded random variables, a value and a timing process, that satisfy the large deviation principle (LDP) with rate function J(⋅,⋅) and whose cumulative process satisfies the LDP with rate function I(⋅). Under mixing conditions, an LDP for estimates of I constructed by transforming an estimate of J is proved. For the case of a cumulative renewal process it is demonstrated that this approach is favourable to a more direct method, as it ensures that the laws of the estimates converge weakly to a Dirac measure at I.

scholarly journals Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains

2018 ◽  
Author(s):  
Rodrigo Cofré ◽  
Cesar Maldonado ◽  
Fernando Rosas
Keyword(s):  
Markov Chains ◽  
Large Deviations ◽  
Maximum Entropy ◽  
Large Deviation ◽  
Spike Trains ◽  
Rate Function ◽  
Statistical Fluctuation ◽  
Maximum Entropy Models ◽  
Deviation Rate Function ◽  
Neuronal Spike Trains

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

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