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Auxiliary Variables in Data Refinement
Information Processing Letters, 1988A set of local variables in a program is auxiliary if its members occur only in assignments to members of the same set. Data refinement transforms a program, replacing one set of local variables by another set, in order to move towards a more efficient representation of data. Most techniques of data refinement give a direct transformation. But there is
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Symbolic traversals of data paths with auxiliary variables
Proceedings of 4th Great Lakes Symposium on VLSI, 2002Symbolic state space traversal techniques are best on control-dominated circuits, not on data paths. This paper extends their applicability to regular structures commonly found in data paths by using auxiliary variables to decompose and to manipulate Boolean functions in decomposed form. Experimental results demonstrate the gain both in terms of binary
CABODI, Gianpiero +2 more
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Journal of Statistical Planning and Inference, 2007
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Gupta, Sat, Shabbir, Javid
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Gupta, Sat, Shabbir, Javid
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A new estimator using two auxiliary variables
Applied Mathematics and Computation, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cem Kadilar, Hulya Cingi
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A Discussion of Auxiliary Variables in Graphing
The Mathematics Teacher, 1977A useful transformation technique for ...
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Scrambled Response Models Based on Auxiliary Variables
2013We discuss the problem of obtaining reliable data on a sensitive quantitative variable without jeopardizing respondent privacy. The information is obtained by asking respondents to perturb the response through a scrambling mechanism. A general device allowing for the use of multi-auxiliary variables is illustrated as well as a class of estimators for ...
Diana G, PERRI, PIER FRANCESCO
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The use of Quantiles of Auxiliary Variables to Estimate Medians
Biometrical Journal, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
del Mar Rueda, María, Arcos, Antonio
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An auxiliary variable method for Langevin based MCMC algorithms
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016Markov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to explore complicated posterior distributions. However, sampling in large scale problems remains a challenging task since the Markov chain is very sensitive to the dependencies between the signal samples.
Marnissi, Yosra +3 more
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Auxiliary variables in partial correctness programming logics
Information Processing Letters, 1989zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Numerical study on the auxiliary entrainment performance of an ejector with different area ratio
Applied Thermal Engineering, 2021Jia Yan, Shengyu Li
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