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Maximum Likelihood Phylogenetic Estimation from DNA Sequences with Variable Rates over Sites: Approximate Methods
 J. Mol. Evol
, 1994
"... Two approximate methods are proposed for maximum likelihood phylogenetic estimation, which allow variable rates of substitution across nucleotide sites. Three data sets with quite different characteristics were analyzed to examine empirically the performance of these methods. The first, called ..."
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Cited by 557 (29 self)
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to their rates predicted assuming the star tree. Sites in different classes are then assumed to be evolving at these fixed rates when other tree topologies are evaluated.
Longitudinal data analysis using generalized linear modelsâ€ť.
 Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
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Cited by 1526 (8 self)
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. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m
Domain names  Implementation and Specification
 RFC883, USC/Information Sciences Institute
, 1983
"... This RFC describes the details of the domain system and protocol, and assumes that the reader is familiar with the concepts discussed in a companion RFC, "Domain Names Concepts and Facilities " [RFC1034]. The domain system is a mixture of functions and data types which are an official pr ..."
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Cited by 725 (9 self)
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This RFC describes the details of the domain system and protocol, and assumes that the reader is familiar with the concepts discussed in a companion RFC, "Domain Names Concepts and Facilities " [RFC1034]. The domain system is a mixture of functions and data types which are an official
Network information flow
 IEEE TRANS. INFORM. THEORY
, 2000
"... We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a pointtopoint communication network on which a number of information sources are to be mulitcast to certain sets of destinations. We assume that the information source ..."
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Cited by 1967 (24 self)
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We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a pointtopoint communication network on which a number of information sources are to be mulitcast to certain sets of destinations. We assume that the information
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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nothing directly to do with coding or decoding will show that in some sense belief propagation "converges with high probability to a nearoptimum value" of the desired belief on a class of loopy DAGs Progress in the analysis of loopy belief propagation has been made for the case of networks
CATH  a hierarchic classification of protein domain structures
 STRUCTURE
, 1997
"... Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structurebased classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can ..."
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Cited by 470 (33 self)
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classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast
Term Premia and Interest Rate Forecasts in Affine Models
, 2001
"... I find that the standard class of a#ne models produces poor forecasts of future changes in Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: The compensation that investors receive for faci ..."
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Cited by 454 (13 self)
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I find that the standard class of a#ne models produces poor forecasts of future changes in Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: The compensation that investors receive
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
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Cited by 440 (17 self)
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In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability
MetaCost: A General Method for Making Classifiers CostSensitive
 In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining
, 1999
"... Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob lems. Individually making each classification learner costsensi ..."
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Cited by 415 (4 self)
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Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob lems. Individually making each classification learner
Combined Object Categorization and Segmentation With An Implicit Shape Model
 In ECCV workshop on statistical learning in computer vision
, 2004
"... We present a method for object categorization in realworld scenes. Following a common consensus in the field, we do not assume that a figureground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach automatical ..."
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Cited by 406 (10 self)
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We present a method for object categorization in realworld scenes. Following a common consensus in the field, we do not assume that a figureground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach
Results 1  10
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