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ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 24 Issue 3, May 2014

Approximate Inference for Observation-Driven Time Series Models with Intractable Likelihoods
Ajay Jasra, Nikolas Kantas, Elena Ehrlich
Article No.: 13
DOI: 10.1145/2592254

In this article, we consider approximate Bayesian parameter inference for observation-driven time series models. Such statistical models appear in a wide variety of applications, including econometrics and applied mathematics. This article...

Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling
Christopher Healey, Sigrún Andradóttir, Seong-Hee Kim
Article No.: 14
DOI: 10.1145/2567921

We consider the problem of selecting the best feasible system with constraints on multiple secondary performance measures. We develop fully sequential indifference-zone procedures to solve this problem that guarantee a nominal probability of...

Space-Time Matching Algorithms for Interest Management in Distributed Virtual Environments
Elvis S. Liu, Georgios K. Theodoropoulos
Article No.: 15
DOI: 10.1145/2567922

Interest management in Distributed Virtual Environments (DVEs) is a data-filtering technique designed to reduce bandwidth consumption and therefore enhances the scalability of the system. This technique usually involves a process called...

Discrete Event Execution with One-Sided and Two-Sided GVT Algorithms on 216,000 Processor Cores
Kalyan S. Perumalla, Alfred J. Park, Vinod Tipparaju
Article No.: 16
DOI: 10.1145/2611561

Global Virtual Time (GVT) computation is a key determinant of the efficiency and runtime dynamics of Parallel Discrete Event Simulations (PDES), especially on large-scale parallel platforms. Here, three execution modes of a generalized GVT...

Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
Article No.: 17
DOI: 10.1145/2628434

Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, especially when the objective is to improve the performance of a stochastic system. However, the performance of these methods...

Selecting Stopping Rules for Confidence Interval Procedures
Dashi I. Singham
Article No.: 18
DOI: 10.1145/2627734

The sample size decision is crucial to the success of any sampling experiment. More samples imply better confidence and precision in the results, but require higher costs in terms of time, computing power, and money. Analysts often choose...