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We propose a variety of models of random walk, discrete in space and time, suitable for simulating stable random variables of arbitrary index $\alpha$ ($0< \alpha \le 2$), in the symmetric case. We show that by properly scaled transition to…

Statistical Mechanics · Physics 2009-10-31 Rudolf Gorenflo , Gianni De Fabritiis , Francesco Mainardi

Accurate spatio-temporal information about the current situation is crucial for smart city applications such as modern routing algorithms. Often, this information describes the state of stationary resources, e.g. the availability of parking…

Artificial Intelligence · Computer Science 2024-04-19 Lukas Rottkamp , Matthias Schubert

We analyze the time reversed dynamics of generative diffusion models. If the exact empirical score function is used in a regime of large dimension and exponentially large number of samples, these models are known to undergo transitions…

Statistics Theory · Mathematics 2025-11-17 Anand Jerry George , Rodrigo Veiga , Nicolas Macris

In this paper, we use a machine learning approach to predict the stationary distributions of the number of customers in a single-staiton multi server system. We consider two systems, the first is $c$ homogeneous servers, namely the…

Performance · Computer Science 2025-04-03 Eliran Sherzer

This paper derives a diffusion approximation for a sequence of discrete-time one-sided limit order book models with non-linear state dependent order arrival and cancellation dynamics. The discrete time sequences are specified in terms of an…

Probability · Mathematics 2017-08-25 Ulrich Horst , Dörte Kreher

Discrete time linear dynamical systems, including Markov chains, have found many applications including in security settings such as in cybersecurity operations center (CSOC) management and in managing health risks. However, in these two…

Optimization and Control · Mathematics 2025-10-28 Nilava Metya , Ankit Shah , Arunesh Sinha

Many records in environmental sciences exhibit asymmetric trajectories and there is a need for simple and tractable models which can reproduce such features. In this paper we explore an approach based on applying both a time change and a…

Methodology · Statistics 2015-10-09 Pierre Ailliot , Bernard Delyon , Valérie Monbet , Marc Prevosto

A G/M/N queue is considered in the moderate deviation heavy traffic regime. The rate function for the customers-in-system process is obtained for the single class model. A risk-sensitive type control problem is considered for multi-class…

Optimization and Control · Mathematics 2013-03-12 Anup Biswas

We study the distribution of the sequence of elements of the discrete dynamical system generated by iterations of the M\"obius map $x \mapsto (ax + b)/(cx+d)$ over a finite field of $p$ elements at the moments of time that correspond to…

Number Theory · Mathematics 2020-09-03 László Mérai , Igor E. Shparlinski

Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion models. However, their convergence properties and error…

Statistics Theory · Mathematics 2026-05-27 Zhengyan Wan , Yidong Ouyang , Qiang Yao , Liyan Xie , Fang Fang , Hongyuan Zha , Guang Cheng

In this paper we analyze a single server queue with batch arrivals and semi-Markovian service times. We also include the feature that the first service of each busy period might have a different distribution than subsequent service times.…

Probability · Mathematics 2018-12-07 Abhishek , Rudesindo Núñez Queija , Marko Boon

We study the $G/\mathit{GI}/\infty$ queue in heavy-traffic using tempered distribution-valued processes which track the age and residual service time of each customer in the system. In both cases, we use the continuous mapping theorem…

Probability · Mathematics 2015-04-22 Josh Reed , Rishi Talreja

We examine a generalised queuing model which we call the G/G/n/G/+ model, which encompasses the G/G/n and G/G/n/s models as special cases. Our model accommodates useful generalisations in user behaviour and limitations on the facilities for…

Computation · Statistics 2021-11-16 Ben O'Neill

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary…

Methodology · Statistics 2025-07-11 Sudipto Banerjee , Xiang Chen , Ian Frankenburg , Daniel Zhou

The stochastic interpolant framework offers a powerful approach for constructing generative models based on ordinary differential equations (ODEs) or stochastic differential equations (SDEs) to transform arbitrary data distributions.…

Machine Learning · Computer Science 2025-07-29 Yuhao Liu , Yu Chen , Rui Hu , Longbo Huang

We consider a Markovian load balancing model on a fully-connected network, where calls have Poisson arrivals and exponential durations. The endpoints of each call are uniform over all the links of the network. Each call is routed either…

Probability · Mathematics 2013-06-24 Malwina Luczak

A new approach to the steady state detection in the uniformization method of solving continuous time Markov chains is introduced. The method is particularly useful in solving inhomogenous CTMC's in multiple steps, where the desired error…

Performance · Computer Science 2014-10-14 Maciej Burak

We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher…

Optimization and Control · Mathematics 2024-08-15 Sara Stoia , Demetrio Laganà , Jeffrey W. Ohlmann

In this paper continuity theorems are established for the number of losses during a busy period of the $M/M/1/n$ queue. We consider an $M/GI/1/n$ queueing system where the service time probability distribution, slightly different in a…

Probability · Mathematics 2008-08-01 Vyacheslav M. Abramov

Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are…

Machine Learning · Computer Science 2021-09-30 Lukas Köhs , Bastian Alt , Heinz Koeppl