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This paper presents exact formulas for the probability distribution function (PDF) and moment generating function (MGF) of the sum-product of statistically independent but not necessarily identically distributed (i.n.i.d.) Nakagami-$m$…

Information Theory · Computer Science 2024-05-28 Hamid Amiriara , Mahtab Mirmohseni , Farid Ashtiani , Masoumeh Nasiri-Kenari

We investigate interference of optical fields by examining the probability distribution of photon detection. The usual description of interference patterns in terms of superposition of classical mean fields with definite phases is…

Quantum Physics · Physics 2014-01-03 Toru Kawakubo , Katsuji Yamamoto

We introduce a class of neural networks derived from probabilistic models in the form of Bayesian networks. By imposing additional assumptions about the nature of the probabilistic models represented in the networks, we derive neural…

Disordered Systems and Neural Networks · Physics 2010-04-30 Michael J. Barber , John W. Clark

To infer a multilayer representation of high-dimensional count vectors, we propose the Poisson gamma belief network (PGBN) that factorizes each of its layers into the product of a connection weight matrix and the nonnegative real hidden…

Machine Learning · Statistics 2015-12-31 Mingyuan Zhou , Yulai Cong , Bo Chen

Geographic locations of cellular base stations sometimes can be well fitted with spatial homogeneous Poisson point processes. In this paper we make a complementary observation: In the presence of the log-normal shadowing of sufficiently…

Networking and Internet Architecture · Computer Science 2015-02-11 Bartlomiej Blaszczyszyn , Mohamed Kadhem Karray , Holger Paul Keeler

Stochastic geometry models of wireless networks based on Poisson point processes are increasingly being developed with a focus on studying various signal-to-interference-plus-noise ratio (SINR) values. We show that the SINR values…

Probability · Mathematics 2014-08-05 Holger Paul Keeler , Bartlomiej Blaszczyszyn

This survey will appear as a chapter of the forthcoming book [19]. A U-statistic of order $k$ with kernel $f:\X^k \to \R^d$ over a Poisson process is defined in \cite{ReiSch11} as$$ \sum\_{x\_1, \dots , x\_k \in \eta^k\_{\neq}} f(x\_1,…

Probability · Mathematics 2015-03-27 Raphaël Lachèze-Rey , Matthias Reitzner

The Poisson distribution of order $k$ is a special case of a compound Poisson distribution. For $k=1$ it is the standard Poisson distribution. Our main result is a proof that for sufficiently small values of the rate parameter $\lambda$,…

Probability · Mathematics 2023-10-03 S. R. Mane

We first describe a general class of optimization problems that describe many natural, economic, and statistical phenomena. After noting the existence of a conserved quantity in a transformed coordinate system, we outline several instances…

Optimization and Control · Mathematics 2018-04-03 David Rushing Dewhurst

The topic of this survey are geometric functionals of a Boolean model (in Euclidean space) governed by a stationary Poisson process of convex grains. The Boolean model is a fundamental benchmark of stochastic geometry and continuum…

Probability · Mathematics 2023-08-14 Daniel Hug , Günter Last , Wolfgang Weil

We derive explicit lower and upper bounds for the probability generating functional of a stationary locally stable Gibbs point process, which can be applied to summary statistics like the F function. For pairwise interaction processes we…

Probability · Mathematics 2013-04-18 Kaspar Stucki , Dominic Schuhmacher

Next-to-leading order parton fragmentation functions into light mesons are presented. They have been extracted from real and simulated $e^+e^-$ data and used to predict inclusive single particle distributions at different machines.

High Energy Physics - Phenomenology · Physics 2007-05-23 Simona Rolli

We propose a general formalism of iterated random functions with semigroup property, under which exact and approximate Bayesian posterior updates can be viewed as specific instances. A convergence theory for iterated random functions is…

Machine Learning · Statistics 2013-11-05 Arash A. Amini , XuanLong Nguyen

The Tweedie Compound Poisson-Gamma model is routinely used for modeling non-negative continuous data with a discrete probability mass at zero. Mixed models with random effects account for the covariance structure related to the grouping…

Machine Learning · Statistics 2019-02-05 Yaodong Yang , Rui Luo , Yuanyuan Liu

A distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel is considered. When the sensor measurements are decreasingly reliable as a function of the sensor index, the conditions on the…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-06-16 Sivaraman Dasarathan , Cihan Tepedelenlioglu

We consider the statistical mechanics of interfering transmissions in a wireless communications protocol. In this case, a connection between two nodes requires all other nodes within communication distance of the given two nodes to remain…

Statistical Mechanics · Physics 2009-11-10 M. B. Hastings

The signal-to-noise-plus-interference ratio (SINR) outage probability is one of the key performance parameters of a wireless cellular network, and its analytical as well as numerical evaluation has occupied many researchers. Recently, the…

Information Theory · Computer Science 2016-07-26 Sudarshan Guruacharya , Hina Tabassum , Ekram Hossain

We study the asymptotic behavior of empirical processes generated by measurable bounded functions of an infinite source Poisson transmission process when the session length have infinite variance. In spite of the boundedness of the…

Probability · Mathematics 2012-07-11 François Roueff , Gennady Samorodnitsky , Philippe Soulier

Approximate outage probability expressions are derived for systems employing maximum ratio combining, when both the desired signal and the interfering signals are subjected to $\eta-\mu$ fading, with the interferers having unequal power.…

Information Theory · Computer Science 2018-05-22 Muralikrishnan Srinivasan , Sheetal Kalyani

We consider arbitrary dense wireless networks, in which $n$ nodes are placed in an arbitrary (deterministic) manner on a square region of unit area and communicate with each other over Gaussian fading channels. We provide inner and outer…

Information Theory · Computer Science 2011-08-09 Urs Niesen