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Auxiliary variables predicting missing data
 Technical appendix. Los Angeles: Muthén & Muthén
, 2008
"... the estimation of structural equation models (SEM) in the presence of missing data. These variables are included in the Mplus analysis using the following command auxiliary=z1 − zk (m). Examples of such analysis can be found in the V5.1 Addendum to the Mplus User’s Guide (Muthen & Muthen 199820 ..."
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the estimation of structural equation models (SEM) in the presence of missing data. These variables are included in the Mplus analysis using the following command auxiliary=z1 − zk (m). Examples of such analysis can be found in the V5.1 Addendum to the Mplus User’s Guide (Muthen & Muthen 1998
— Auxiliary variables — Normalizing constants — References
"... Construct a biased random walk that explores a target dist. Markov steps, x (s) ∼ T ( x (s) ←x (s−1)) MCMC gives approximate, correlated samples ..."
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Construct a biased random walk that explores a target dist. Markov steps, x (s) ∼ T ( x (s) ←x (s−1)) MCMC gives approximate, correlated samples
Reusing Auxiliary Variables for MaxSAT Preprocessing
"... Abstract—Solvers for the maximum satisfiability (MaxSAT) problem—a wellknown optimization variant of Boolean satisfiability (SAT)—are finding an increasing number of applications. Preprocessing has proven an integral part of the SATbased approach to efficiently solving various types of realworl ..."
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world problem instances. It was recently shown that SAT preprocessing for MaxSAT becomes more effective by reusing the auxiliary variables introduced in the preprocessing phase directly in the SAT solver within a corebased hybrid MaxSAT solver. We take this idea of reusing auxiliary variables further
RealTime Program Refinement Using Auxiliary Variables
 SIXTH INTERNATIONAL SCHOOL AND SYMPOSIUM ON FORMAL TECHNIQUES IN REALTIME AND FAULTTOLERANT SYSTEMS (FTRTFT 2000), VOLUME 1926 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2000
"... Realtime program development can be split into a machineindependent phase, that derives a machineindependent realtime program from a specification, and a machinedependent phase, that checks that the compiled program will meet its deadlines when executed on the target machine. In this paper ..."
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we extend a machineindependent realtime programming language with auxiliary variables. These are introduced to facilitate both reasoning about the correctness of realtime programs and the expression of timing deadlines, and hence the calculation of timing constraints on paths through a program
Bayesian Auxiliary Variable Models for Binary and Multinomial Regression
"... Abstract. In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of ..."
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Abstract. In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance
A PPS Sampling Scheme Using Harmonic Mean of an Auxiliary Variable
 ARPN Journal of Systems and Software
, 2012
"... We consider a probability proportional to size sampling scheme by using harmonic mean of an auxiliary variable, when the correlation between study variable and auxiliary variable is highly negative. This is achieved by considering inverse transformation of the auxiliary variable and then utilizing t ..."
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We consider a probability proportional to size sampling scheme by using harmonic mean of an auxiliary variable, when the correlation between study variable and auxiliary variable is highly negative. This is achieved by considering inverse transformation of the auxiliary variable and then utilizing
Improving inference of gaussian mixtures using auxiliary variables
, 2012
"... Expanding a lowerdimensional problem to a higherdimensional space and then projecting back is often beneficial. This article rigorously investigates this perspective in the context of finite mixture models, namely how to improve inference for mixture models by using auxiliary variables. Despite th ..."
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Expanding a lowerdimensional problem to a higherdimensional space and then projecting back is often beneficial. This article rigorously investigates this perspective in the context of finite mixture models, namely how to improve inference for mixture models by using auxiliary variables. Despite
On the Use of Auxiliary Variables in Agricultural Surveys Design
"... Auxiliary variables, both univariate and multivariate, must be efficiently used to obtain accurate estimates. They are useful ex ante, that is when the sample has to be drawn, but also ex post, as the weight calibration method. The classical issue on efficient sample design through a stratification ..."
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Auxiliary variables, both univariate and multivariate, must be efficiently used to obtain accurate estimates. They are useful ex ante, that is when the sample has to be drawn, but also ex post, as the weight calibration method. The classical issue on efficient sample design through a stratification
On the Use of Auxiliary Variables in Agricultural Surveys Design
"... Auxiliary variables, both univariate and multivariate, must be efficiently used to obtain accurate estimates. They are useful ex ante, that is when the sample has to be drawn, but also ex post, as the weight calibration method. The classical issue on efficient sample design through a stratification ..."
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Cited by 1 (1 self)
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Auxiliary variables, both univariate and multivariate, must be efficiently used to obtain accurate estimates. They are useful ex ante, that is when the sample has to be drawn, but also ex post, as the weight calibration method. The classical issue on efficient sample design through a stratification
HigherOrder Clique Reduction Without Auxiliary Variables
"... We introduce a method to reduce most higherorder terms of Markov Random Fields with binary labels into lowerorder ones without introducing any new variables, while keeping the minimizer of the energy unchanged. While the method does not reduce all terms, it can be used with existing techniques tha ..."
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that transforms arbitrary terms (by introducing auxiliary variables) and improve the speed. The method eliminates a higherorder term in the polynomial representation of the energy by finding the value assignment to the variables involved that cannot be part of a global minimizer and increasing the potential
Results 11  20
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1,533