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The Stochastic Block Model (Holland et al., 1983) is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information about the previous ones in this model. Thereby the…

Statistics Theory · Mathematics 2011-11-01 Antoine Channarond , Jean-Jacques Daudin , Stéphane Robin

Stochastic process discovery is concerned with deriving a model capable of reproducing the stochastic character of observed executions of a given process, stored in a log. This leads to an optimisation problem in which the model's parameter…

Formal Languages and Automata Theory · Computer Science 2025-05-01 Pierre Cry , Paolo Ballarini , András Horváth , Pascale Le Gall

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost…

Optimization and Control · Mathematics 2014-10-17 Stefan Streif , Matthias Karl , Ali Mesbah

The vast majority of stochastic simulation models are imperfect in that they fail to exactly emulate real system dynamics. The inexactness of the simulation model, or model discrepancy, can impact the predictive accuracy and usefulness of…

Methodology · Statistics 2017-07-21 Matthew Plumlee , Henry Lam

Machine learning models are often tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of weights and…

Machine Learning · Computer Science 2018-03-09 Jonathan Lorraine , David Duvenaud

To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables…

Artificial Intelligence · Computer Science 2009-03-09 Toby Walsh

We review studies based on analytic and simulation methods for hierarchical performance analysis of Queueing Network - QN models, which result in an order of magnitude reduction in performance evaluation cost with respect to simulation. The…

Performance · Computer Science 2024-01-18 Alexander Thomasian

Time-Sensitive Networking (TSN) is an enhancement of Ethernet which provides various mechanisms for real-time communication. Time-triggered (TT) traffic represents periodic data streams with strict real-time requirements. Amongst others,…

Networking and Internet Architecture · Computer Science 2023-07-31 Thomas Stüber , Lukas Osswald , Steffen Lindner , Michael Menth

We consider a multi-class single server queueing network as a model of a packet switching network. The rates packets are sent into this network are controlled by queues which act as congestion windows. By considering a sequence of…

Optimization and Control · Mathematics 2010-11-25 Neil Stuart Walton

As data volume grows extensively, data profiling helps to extract metadata of large-scale data. However, one kind of metadata, order statistics, is difficult to be computed because they are not mergeable or incremental. Thus, the limitation…

Data Structures and Algorithms · Computer Science 2020-06-29 Zhiwei Chen , Aoqian Zhang

An innovative numerical technique is presented to adjust the inflow to a supply chain in order to achieve a desired outflow, reducing the costs of inventory, or the goods timing in warehouses. The supply chain is modelled by a conservation…

Numerical Analysis · Mathematics 2015-03-20 Ciro D'Apice , Rosanna Manzo , Benedetto Piccoli

The stochastic block model is able to generate different network partitions, ranging from traditional assortative communities to disassortative structures. Since the degree-corrected stochastic block model does not specify which mixing…

Social and Information Networks · Computer Science 2019-09-16 Xiaoyan Lu , Boleslaw K. Szymanski

We present the Stochastic alternate Linearization Method (StochaLM), a token-based method for distributed optimization. This algorithm finds the solution of a consensus optimization problem by solving a sequence of subproblems where some…

Signal Processing · Electrical Eng. & Systems 2021-12-28 Inês Almeida , João Xavier

In this paper, an approach to estimating a nonlinear deterministic model is presented. We introduce a stochastic model with extremely small variances so that the deterministic and stochastic models are essentially indistinguishable from…

Methodology · Statistics 2015-11-13 Spyridon J. Hatjispyros , Stephen G. Walker

In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external…

Machine Learning · Computer Science 2016-01-06 John Schulman , Nicolas Heess , Theophane Weber , Pieter Abbeel

The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer models are becoming stochastic, yielding different outputs…

Methodology · Statistics 2020-04-10 Evan Baker , Peter Challenor , Matt Eames

Three classes of stochastic networks and their performance measures are considered. These performance measures are defined as the expected value of some random variables and cannot normally be obtained analytically as functions of network…

Optimization and Control · Mathematics 2012-10-24 Nikolai Krivulin

In this paper we propose a stochastic broadcast PI-calculus which can be used to model server-client based systems where synchronization is always governed by only one participant. Therefore, there is no need to determine the joint…

Performance · Computer Science 2011-07-07 Lei Song , Flemming Nielson , Bo Friis Nielsen

This report presents a novel approach to calculate volumes of trains from a set of requirements describing the transportation demands. The model presented is based on a multi commodity network flow model. The model comprises three different…

Optimization and Control · Mathematics 2021-12-20 Martin Aronsson , Tomas Lidén

A multilevel network is defined as the junction of two interaction networks, one level representing the interactions between individuals and the other the interactions between organizations. The levels are linked by an affiliation…

Methodology · Statistics 2023-12-04 Saint-Clair Chabert-Liddell , Pierre Barbillon , Sophie Donnet , Emmanuel Lazega
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