5 edition of **Large deviations for Gaussian queues** found in the catalog.

Large deviations for Gaussian queues

Michel Mandjes

- 324 Want to read
- 34 Currently reading

Published
**2008** by John Wiley & Sons, Ltd in Chichester, UK, Hoboken, NJ .

Written in English

**Edition Notes**

Statement | Michel Mandjes. |

Classifications | |
---|---|

LC Classifications | QA |

The Physical Object | |

Pagination | x, 326 p. : |

Number of Pages | 326 |

ID Numbers | |

Open Library | OL22760876M |

ISBN 10 | 9780470015230 |

This paper discusses the large-deviation principle of discriminant statistics for Gaussian locally stationary processes. First, large-deviation theorems for quadratic forms and the log-likelihood ratio for a Gaussian locally stationary process with a mean function are proved. Their asymptotics are described by the large deviation rate : Junichi Hirukawa. Information Theory and Wireless Networking. Search this site. Home. Book. Journal. Two-parameter Sample Path Large Deviations for Infinite Server Queues. Stochastic Systems, 4, (), Blanchet, J. and Lam, H. Uniform Large Deviations for Heavy-Tailed Queues under Heavy-Traffic. Bulletin of the Mexican Mathematical Society, Bol. Soc. Mat. Mexicana (3) Vol. 19, Special Issue for the International Year of.

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Large Deviations for Gaussian Queues demonstrates how the Gaussian traffic model arises naturally, and how the analysis of the corresponding queuing model can be performed. The text provides a general introduction to Gaussian queues, and surveys recent research into the modelling of communications networks.

Large Deviations for Gaussian Queues demonstrates how the Gaussian traffic model arises naturally, and how the analysis of the corresponding queuing model can be performed.

The book Author: Michel Mandjes. Part B: Large deviations of Gaussian queues. 5 Gaussian queues: an introduction. Lindley's recursion, the steady-state buffer content.

Gaussian queues. Special cases: Brownian motion and Brownian bridge. A powerful approximation. Asymptotics. Large-buffer asymptotics.

6 Logarithmic many-sources asymptotics. Many. Large Deviations for Gaussian Queues assumes minimal prior knowledge. It is ideally suited for postgraduate students in applied probability, operations research, computer science and electrical engineering.

The book's self-contained style makes it perfect for practitioners in the communications networking industry and for researchers in related.

Large Deviations for Gaussian Queues demonstrates how the Gaussian traffic model arises naturally, and how the analysis of the corresponding queuing model can be performed. The text provides a general introduction to Gaussian queues, and surveys recent research into the modelling of communications by: Michel Mandjes - Large deviations for gaussian queues.

Modelling communication networks ( Wiley).pdf код для вставки. T1 - Sample-path large deviations for tandem queues with Gaussian inputs. AU - Mandjes, M.R.H.

AU - van Uitert, Miranda. PY - /8. Y1 - /8. N2 - We focus on a (two-node) tandem queue, Large deviations for Gaussian queues book by a large number of Gaussian by: 3.

Sample-path large deviations for tandem and priority queues with Gaussian inputs Article in The Annals of Applied Probability 15(2) June with 6 Reads How we measure 'reads'.

Large deviations. Above we motivated the interest in GPS queues with Gaussian inputs. Our study focuses on a large-deviations analysis of this model. Over the past two decades, significant research efforts have been made on the large-deviations analysis of queueing by: In probability theory, the theory of large deviations concerns the asymptotic behaviour of remote tails of sequences of probability distributions.

While some basic ideas of the theory can be traced to Laplace, the formalization started with insurance mathematics, namely ruin theory with Cramér and Lundberg.A unified formalization of large deviation theory was developed inin a.

() Large deviations, moderate deviations, and queues with long-range dependent input. Advances in Applied Probability() Rough descriptions of ruin for a general class of surplus by: Sample-path large deviations for tandem queues with Gaussian inputs Michel Mandjes ab and Miranda van Uitert ~ a CWI, P.O.

BoxGB Amsterdam, the Netherlands. b University of Twente, Department of Applied Mathematics, P.O. BoxAE Enschede, the Netherlands 1 We focus on a (two-node) tandem queue, fed by a large number of Cited by: 3.

SAMPLE-PATH LARGE DEVIATIONS FOR TANDEM AND PRIORITY QUEUES WITH GAUSSIAN INPUTS BY MICHEL MANDJES1 AND MIRANDA VANUITERT CWI and University of Twente, and CWI and Vrije Universiteit This paper considers Gaussian ﬂows multiplexed in a queueing network.

A single node being a useful but often Large deviations for Gaussian queues book setting, we examine.

His research focuses on queueing theory and stochastic process analysis, with operations-research-type applications. He is author of the book Large Deviations for Gaussian Queues.

He serves as an associate editor of Queueing Systems, Stochastic Systems, Stochastic Models, and Advances in Applied Probability / Journal of Applied Probability. Get this from a library.

Large deviations for Gaussian queues: modelling communication networks. [Michel Mandjes] -- This book describes how modern queuing theory can be applied to problems in telecommunication engineering.

It starts with a survey of the essential theory behind Gaussian processes, large deviations. In this paper we derive large-buffer asymptotics for a two-class Generalized Processor Sharing (GPS) model.

We assume both classes to have Gaussian characteristics. We distinguish three cases depending on whether the GPS weights are above or below the average rate at which traffic is sent. First, we calculate exact asymptotic upper and lower bounds, then Cited by: Large deviations for Markovian nonlinear Hawkes processes Zhu, Lingjiong, The Annals of Applied Probability, ; Central limit theorem for nonlinear Hawkes processes Zhu, Lingjiong, Journal of Applied Probability, ; Inference for a nonstationary self-exciting point process with an application in ultra-high frequency financial data modeling Chen, Feng and Hall, Peter, Cited by: The objective of this chapter is to demonstrate the development of a large deviations theory for processes with long range dependence.

Our starting point is the integral relationship between the standard Brownian motion, B, and the fractional Brownian motion, B Cited by: 2. Large Deviations for a Gaussian random variable Let X be a standard Gaussian random variable: X ∼ N(0,1), with density function p(x) = 1.

() Large deviations for sample paths of quadratic variations of Gaussian processes. Journal of Mathematical Sciences() Sample path large deviations for a family of long-range dependent traffic and associated queue length by: Large Deviations for Gaussian Queues: Modelling Communication Networks by Michel Mandjes.

Wiley, Hardcover. Good. Large deviations for sample paths of Gaussian processes quadratic variations Olivier Perrin Marguerite Zani y March 4, Abstract We show a functional large deviations principle for the family of randomfunctions n Vn(x) = P[nx] k=1(Zk=n Zk 1=n) 2;x2 [0;1] o, where fZt;t2 [0;1]g is a real valued centered Gaussian process.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY /J Fall Lecture 2 9/9/ Large Deviations for i.i.d. Random Variables. Content. Chernoff bound using exponential moment generating functions.

Properties of a moment generating functions. Legendre transforms. Preliminary notes. The Weak Law of Large Numbers tells us that if X 1,XFile Size: KB.

Basic statement. Let be the maximal value of a Gaussian random function on the (two-dimensional) sphere. Assume that the expected value of is (at every point of the sphere), and the standard deviation of is (at every point of the sphere).

Then, for large >, (>) is close to (− /) + (>), where is distributed (,) (the standard normal distribution), and is a constant; it does not. On large deviations of empirical measures for stationary Gaussian processes Wtodzimierz Bryc a'*, Amir Dembo b'l aDepartment of Mathematics, University of Cincinnati, OH 45USA bDepartment of Mathematics and Department of Statistics, Stanford University, Stanford, CA.

Large Deviations for Gaussian Queues - Modelling Communication Networks Part A: Gaussian traffic and large deviations. 2 The Gaussian source. On Gaussian Limits and Large Deviations for Queues Fed by High Intensity Randomly Scattered Tra c Peter W. Glynn Harsha Honnappa Abstract We study a single server FIFO queue that o ers general service.

Each of ncustomers enter the queue at random time epochs that are independent and identically distributed. Resume and publications. Find here my full CV, also containing a year-by-year publication list. If you want to have a copy of one of my papers, please let me know.

My email address is [email protected] Many of my papers can be found on [Publications CWI Michel Mandjes]. Large deviation estimates of the crossing probability for pinned Gaussian processes Lucia Caramellino Barbara Pacchiarotti Dept. of Mathematics, University of Rome-Tor Vergata mailto: fcaramell,[email protected] 31st March Abstract.

The paper deals with the asymptotic behavior of the bridge of a Gaussian process. $\begingroup$ Somehow it doesn't make sense to "calculate $\mathbb{P}(X \geq 5)$ by means of large deviations".

Large deviation theory is always about the exponential decay of probabilities, i.e. estimates of the form $$\mathbb{P}(X_n \in B) \sim \exp(-n I(B));$$ this means in particular that the probabiliy converges to $0$. I'm reading the classic book on Large Deviations: Large Deviations by Dembo and Zeitouni.

On page $$, Lemma $$, they make a proof which is purely stochastic calculus with no tricks. stochastic-calculus stochastic-integrals stochastic-analysis large-deviation-theory.

Fluctuations and large deviations of some perturbed random matrices MYLÈNE MAÏDA We review joint results with Benaych-Georges and Guionnet (Electron. Probab. (), – and Prob. Theory Rel. Fields –4 (), –) about ﬂuctuations and large deviations of some spiked. Michel Mandjes. 12 likes. Michael Robertus Hendrikus "Michel" Mandjes is a Dutch mathematician, known for several contributions to queueing theory and Jump to.

Sections of this page. is author of the book "Large deviations for Gaussian queues", and is associate editor of the journals Stochastic Models and Queuing.

He is author of the book "Large deviations for Gaussian queues", and is associate editor of the journals Stochastic Models and Queuing Systems. [citation needed] He contributed to the book Queues and Lévy fluctuation theory, published in Born: Febru (age 49), Zaandam, the.

The Large Deviation Principle is deriv ed for sev eral un b ounded additiv e functionals of cen-tered stationary Gaussian pro cesses. F or example, the rate function corresp onding to 1 T R T 0 X 2 t dt is the F enc hel-Legendre transform of L (y)= 1 4 R 1 1 log (1 4 yf s)) ds, where X t is a con tin-uous time pro cess with the b ounded sp.

This is the famous Gaussian distribution function, named after the German mathematician Carl Friedrich Gauss, who discovered it whilst investigating the distribution of errors in Gaussian distribution is only valid in the limits and. Suppose we were to plot the probability against the integer variable, and then fit a continuous curve through the.

RANDOM WALKS, LARGE DEVIATIONS, AND MARTINGALES Sections and treat this same question for general random walks, but the results are far less simple.

They also treat questions such as the overshoot given a threshold crossing, the time at which the threshold is crossed given that it is crossed, and the probability of. 2 Large deviations for locally stationary Gaussian processes We begin this section with some definitions.

Let Pn and Qn be probability measures deﬁned on some space (n,Fn). For each n, suppose that Qn is absolutely continuous w.r.t.

Pn (in short notation Qn Pn). Let n = log dQn dPn be the log-likelihood ratio between Qn and. This book serves both as an introduction to the subject and as a basic reference text. The familiar Gaussian models are named after the German mathematician Carl Friedrich Gauss.

Based on the usual "bell-shaped" probability curve, they do not allow for large deviations and are thus often inadequate for modelling high variability. It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications.

The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. The book is highly technical but it also does a great job explaining how Gaussian Processes fit in the big picture regarding the last few decades in the Machine Learning field and how they are related in some ways to both SVM and Neural Networks.

I'm still working my way through the book but so far I'm extremely pleased with by: Large deviations for Gaussian queues M.

Mandjes John Wiley & Sons, Ltd. 1 COPYRIGHTED MATERIAL. Introduction is the characterization of the impact of ‘user parameters’ on the performance offered by the network (how is the delay affected by the arrival rate?

what is the impact of.Large Deviations For Performance Analysis Queues, Communication and Computing, 1st Edition. By Alan Weiss, Adam Shwartz. Originally published inLarge Deviations for Performance Analysis consists of two synergistic parts.