Results 11  20
of
6,553
Research Article A Survey Design for a Sensitive Binary Variable Correlated with Another Nonsensitive Binary Variable
"... Copyright © 2013 JunWu Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tian et al. (2007) introduced a socalled hidden se ..."
Abstract
 Add to MetaCart
sensitivity model for evaluating the association of two sensitive questions with binary outcomes. However, in practice, we sometimes need to assess the association between one sensitive binary variable (e.g., whether or not a drug user, the number of sex partner being ⩽1 or>1, and so on) and one
The ratedistortion function for source coding with side information at the decoder
 IEEE Trans. Inform. Theory
, 1976
"... AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a seque ..."
Abstract

Cited by 1060 (1 self)
 Add to MetaCart
AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a
A Binary Variable Model for Affinity Propagation
, 2009
"... Affinity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplarbased clustering. We present a derivation of AP that is much simpler than the original one and is based on a quite different graphical model. The new model allows easy derivations of message updates ..."
Abstract

Cited by 22 (3 self)
 Add to MetaCart
Affinity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplarbased clustering. We present a derivation of AP that is much simpler than the original one and is based on a quite different graphical model. The new model allows easy derivations of message updates for extensions and modifications of the standard AP algorithm. We demonstrate this by adjusting the new AP model to represent the capacitated clustering problem. For those wishing to investigate or extend the graphical model of the AP algorithm, we suggest using this new formulation since it allows for simpler and more intuitive model manipulation. 1 1
Glymour: Linearity properties of Bayes nets with binary variables
 Uncertainty in Artificial Intelligence: Proceedings of the 17th Conference (UAI2001
, 2001
"... It is “well known ” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables ” sometimes permits an estimation ..."
Abstract

Cited by 13 (9 self)
 Add to MetaCart
It is “well known ” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables ” sometimes permits an estimation
A Discrete Binary Version of The Particle Swarm Algorithm
 PROC. OF CONF. ON SYSTEM, MAN, AND CYBERNETICS, 4104–4109
, 1997
"... The particle swarm algorithm adjusts the trajectories of a population of “particles” through a problem space on the basis of information about each particle’s previous best performance and the best previous performance of its neighbors. Previous versions of the particle swarm have operated in contin ..."
Abstract

Cited by 339 (2 self)
 Add to MetaCart
in continuous space, where trajectories are defined as changes in position on some number of dimensions. The present paper reports a reworking of the algorithm to operate on discrete binary variables. In the binary version, trajectories are changes in the probability that a coordinate will take on a zero or one
Optimizing A Unimodal Response Function For Binary Variables
, 2000
"... Several allocation rules are examined for the problem of optimizing a response function for a set of Bernoulli populations, where the population means are assumed to have a strict unimodal structure. This problem arises in dose response settings in clinical trials. The designs are evaluated both on ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
Several allocation rules are examined for the problem of optimizing a response function for a set of Bernoulli populations, where the population means are assumed to have a strict unimodal structure. This problem arises in dose response settings in clinical trials. The designs are evaluated both on their efficiency in identifying a good population at the end of the experiment, and in their efficiency in sampling from good populations during the trial. A new design, that adapts multiarm bandit strategies to this unimodal structure, is shown to be superior to the designs previously proposed. The bandit design utilizes approximate Gittin's indices and shape constrained regression. Keywords: nonparametric, adaptive, sequential sampling, experimental design, doseresponse, clinical trial, multiarm bandit, up and down, random walk, stochastic approximation, Polya urn, unimodal regression Introduction Consider a problem in which there are k linearly ordered populations or "arms". Assoc...
Measuring Morbidity: Disease Counts, Binary Variables, and Statistical Power
"... This study compares the use of the binary disease variables with counts of the same conditions in models of selfrated health to better understand the advantages and disadvantages of each approach. In particular, the analysis seeks to determine if statistical power is adequate for the binary variabl ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
This study compares the use of the binary disease variables with counts of the same conditions in models of selfrated health to better understand the advantages and disadvantages of each approach. In particular, the analysis seeks to determine if statistical power is adequate for the binary
A LowCost Decoder for Arbitrary Binary VariableLength Codes
, 1997
"... Encoders and decoders for variablelength codes such as Huffman Codes can be costly to implement. This paper describes lowcost encoder and decoder for binary variablelength codes that is simple to implement when decoding speed is not an issue. ..."
Abstract
 Add to MetaCart
Encoders and decoders for variablelength codes such as Huffman Codes can be costly to implement. This paper describes lowcost encoder and decoder for binary variablelength codes that is simple to implement when decoding speed is not an issue.
Published In Linearity Properties of Bayes Nets with Binary Variables
, 2001
"... It is “well known ” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables ” sometimes permits an estimation ..."
Abstract
 Add to MetaCart
It is “well known ” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables ” sometimes permits
Results 11  20
of
6,553