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Input Modeling

by Lawrence Leemis , 2003
"... Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. The genera ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling

Input Modeling

by D. J. Medeiros, E. F. Watson, J. S. Carson, M. S. Manivannan, Lawrence Leemis , 1998
"... Discrete-event simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The genera ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
Discrete-event simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system

Input Modeling

by J. A. Joines, R. R. Barton, K. Kang, P. A. Fishwick, Lawrence Leemis , 2000
"... Discrete-event simulation models typically have stochastic elements that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The general ..."
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Discrete-event simulation models typically have stochastic elements that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The general

input modeling

by Faker Zouaoui, James R. Wilson , 2001
"... Accounting for parameter uncertainty in simulation ..."
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Accounting for parameter uncertainty in simulation

Hierarchical Models of Object Recognition in Cortex

by Maximilian Riesenhuber, Tomaso Poggio , 1999
"... The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore th ..."
Abstract - Cited by 836 (84 self) - Add to MetaCart
predictions. The model is based on a novel MAX-like operation on the inputs to certain cortical neurons which may have a general role in cortical function.

A distributed, developmental model of word recognition and naming

by Mark S. Seidenberg, James L. McClelland - PSYCHOLOGICAL REVIEW , 1989
"... A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagatio ..."
Abstract - Cited by 706 (49 self) - Add to MetaCart
is simulated without pronunciation rules, and lexical decisions are simulated without accessing word-level representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit

Bandera: Extracting Finite-state Models from Java Source Code

by James C. Corbett, Matthew B. Dwyer, John Hatcliff, Shawn Laubach, Corina S. Pasareanu, Hongjun Zheng - IN PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING , 2000
"... Finite-state verification techniques, such as model checking, have shown promise as a cost-effective means for finding defects in hardware designs. To date, the application of these techniques to software has been hindered by several obstacles. Chief among these is the problem of constructing a fini ..."
Abstract - Cited by 654 (33 self) - Add to MetaCart
program source code. Bandera takes as input Java source code and generates a program model in the input language of one of several existing verification tools; Bandera also maps verifier outputs back to the original source code. We discuss the major components of Bandera and give an overview of how it can

Fitting a mixture model by expectation maximization to discover motifs in biopolymers.

by Timothy L Bailey , Charles Elkan - Proc Int Conf Intell Syst Mol Biol , 1994
"... Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to th ..."
Abstract - Cited by 947 (5 self) - Add to MetaCart
to the data, probabilistically erasing tile occurrences of the motif thus found, and repeating the process to find successive motifs. The algorithm requires only a set of unaligned sequences and a number specifying the width of the motifs as input. It returns a model of each motif and a threshold which

Simulation Input Modeling

by Lawrence Leemis , 1999
"... Discrete-event simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The genera ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Discrete-event simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system

BUILDING CREDIBLE INPUT MODELS

by R. G. Ingalls, M. D. Rossetti, J. S. Smith, B. A. Peters, Lawrence M. Leemis
"... Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. The genera ..."
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Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling
Results 1 - 10 of 56,602