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Error Bounds and Improved Probability Estimation using the Maximum Likelihood Set
, 2007
"... Abstract — The maximum likelihood set (MLS) is a novel candidate for nonparametric probability estimation from small samples that permits incorporating prior or structural knowledge into the estimator [1]. It is a set of probability distributions which assign to the observed type (or empirical distr ..."
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Abstract — The maximum likelihood set (MLS) is a novel candidate for nonparametric probability estimation from small samples that permits incorporating prior or structural knowledge into the estimator [1]. It is a set of probability distributions which assign to the observed type (or empirical
Language Modeling with the Maximum Likelihood Set: Complexity Issues and the Backoff Formula
"... Abstract — The Maximum Likelihood Set (MLS) was recently introduced in [1] as an effective, parameterfree technique for estimating a probability mass function (pmf) from sparse data. The MLS contains all pmfs that assign merely a higher likelihood to the observed counts than to any other set of cou ..."
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Abstract — The Maximum Likelihood Set (MLS) was recently introduced in [1] as an effective, parameterfree technique for estimating a probability mass function (pmf) from sparse data. The MLS contains all pmfs that assign merely a higher likelihood to the observed counts than to any other set
LETTER Communicated by Liam Paninski Maximum Likelihood Set for Estimating a Probability Mass Function
"... We propose a new method for estimating the probability mass function (pmf) of a discrete and finite random variable from a small sample. We focus on the observed counts—the number of times each value appears in the sample—and define the maximum likelihood set (MLS) as the set of pmfs that put more m ..."
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We propose a new method for estimating the probability mass function (pmf) of a discrete and finite random variable from a small sample. We focus on the observed counts—the number of times each value appears in the sample—and define the maximum likelihood set (MLS) as the set of pmfs that put more
Object class recognition by unsupervised scaleinvariant learning
 In CVPR
, 2003
"... We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and ..."
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Cited by 1127 (50 self)
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and relative scale. An entropybased feature detector is used to select regions and their scale within the image. In learning the parameters of the scaleinvariant object model are estimated. This is done using expectationmaximization in a maximumlikelihood setting. In recognition, this model is used in a
Paml 4: Phylogenetic analysis by maximum likelihood
 Mol. Biol. Evol
, 2007
"... PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which ..."
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Cited by 1201 (28 self)
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PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2182 (27 self)
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of distancebased and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximumlikelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting
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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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
Quartet puzzling: a quartet maximum likelihood method for reconstructing tree topologies.
 Mol. Biol. Evol.
, 1996
"... A versatile method, quartet puzzling, is introduced to reconstruct the topology (branching pattern) of a phylogenetic tree based on DNA or amino acid sequence data. This method applies maximumlikelihood tree reconstruction to all possible quartets that can be formed from n sequences. The quartet t ..."
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Cited by 433 (9 self)
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A versatile method, quartet puzzling, is introduced to reconstruct the topology (branching pattern) of a phylogenetic tree based on DNA or amino acid sequence data. This method applies maximumlikelihood tree reconstruction to all possible quartets that can be formed from n sequences. The quartet
Probabilistic Principal Component Analysis
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
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Cited by 709 (5 self)
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Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 693 (4 self)
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rigor. ii 1 Maximumlikelihood Recall the definition of the maximumlikelihood estimation problem. We have a density function ¢¡¤£¦ ¥ §© ¨ that is governed by the set of parameters § (e.g., might be a set of Gaussians and § could be the means and covariances). We also have a data set of size
Results 1  10
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