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455
Discriminative fields for modeling spatial dependencies in natural images
 In NIPS
, 2003
"... In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to ..."
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Cited by 145 (4 self)
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to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudolikelihood method. Furthermore, the form of the DRF model allows the MAP inference
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex but the memor ..."
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Cited by 334 (2 self)
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We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex
Estimation of sparse binary pairwise Markov networks using pseudolikelihood
 J
"... We consider the problems of estimating the parameters as well as the structure of binaryvalued Markov networks. For maximizing the penalized loglikelihood, we implement an approximate procedure based on the pseudolikelihood of Besag (1975) and generalize it to a fast exact algorithm. The exact al ..."
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Cited by 41 (0 self)
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We consider the problems of estimating the parameters as well as the structure of binaryvalued Markov networks. For maximizing the penalized loglikelihood, we implement an approximate procedure based on the pseudolikelihood of Besag (1975) and generalize it to a fast exact algorithm. The exact
Optimization methods for sparse pseudolikelihood graphical model selection
 in Advances in Neural Information Processing Systems 27
, 2014
"... Sparse high dimensional graphical model selection is a popular topic in contemporary machine learning. To this end, various useful approaches have been proposed in the context of `1penalized estimation in the Gaussian framework. Though many of these inverse covariance estimation approaches are dem ..."
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Cited by 1 (1 self)
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proaches are demonstrably scalable and have leveraged recent advances in convex optimization, they still depend on the Gaussian functional form. To address this gap, a convex pseudolikelihood based partial correlation graph estimation method (CONCORD) has been recently proposed. This method uses coordinate
A path following algorithm for Sparse PseudoLikelihood Inverse Covariance Estimation (SPLICE)
, 2008
"... Given n observations of a pdimensional random vector, the covariance matrix and its inverse (precision matrix) are needed in a wide range of applications. Sample covariance (e.g. its eigenstructure) can misbehave when p is comparable to the sample size n. Regularization is often used to mitigate th ..."
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Cited by 20 (0 self)
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the problem. In this paper, we proposed an ℓ1 penalized pseudolikelihood estimate for the inverse covariance matrix. This estimate is sparse due to the ℓ1 penalty, and we term this method SPLICE. Its regularization path can be computed via an algorithm based on the homotopy/LARSLasso algorithm. Simulation
Fourth Order Pseudo Maximum Likelihood Methods
, 2008
"... are grateful to Prof. W. Gautschi for his advice concerning numerical integration and to Prof. G. V. Milovanović for providing a very useful sequence of parameters used for numerical integrations. ..."
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are grateful to Prof. W. Gautschi for his advice concerning numerical integration and to Prof. G. V. Milovanović for providing a very useful sequence of parameters used for numerical integrations.
ON METHODS OF SIEVES AND PENALIZATION
, 1997
"... We develop a general theory which provides a unified treatment for the asymptotic normality and efficiency of the maximum likelihood estimates (MLE’s) in parametric, semiparametric and nonparametric models. We find that the asymptotic behavior of substitution estimates for estimating smooth function ..."
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Cited by 61 (1 self)
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efficiency. When the size of the parameter space is very large, the standard and penalized maximum likelihood procedures may be inefficient, whereas the method of sieves may be able to overcome this difficulty. This phenomenon is particularly manifested when the functional of interest is very smooth
Estimation of TailRelated Risk Measures for Heteroscedastic Financial Time Series: an Extreme Value Approach
 Journal of Empirical Finance
, 1998
"... We propose a method for estimating VaR and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudomaximumlikelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) ..."
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Cited by 239 (6 self)
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We propose a method for estimating VaR and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudomaximumlikelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT
On Approximate Pseudo Maximum Likelihood Estimation for LARCHProcesses
"... Linear ARCH (LARCH) processes have been introduced by Robinson (1991) to model longrange dependence in volatility and leverage. Basic theoretical properties of LARCH processes have been investigated in the recent literature. However, there is a lack of estimation methods and corresponding asympto ..."
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Cited by 1 (1 self)
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asymptotic theory. In this paper, we consider estimation of the dependence parameters for LARCH processes with nonsummable hyperbolically decaying coecients. Asymptotic limit theorems are derived. A central limit theorem with p nrate of convergence holds for an approximate conditional pseudolikelihood
Maximum Pseudo Likelihood Estimation in Network Tomography
"... Abstract — Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today’s fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely ..."
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. In this paper, a pseudo likelihood approach is proposed to solve a group of network tomography problems. The basic idea of pseudo likelihood is to form simple subproblems and ignore the dependences among the subproblems to form a product likelihood of the subproblems. As a result, this approach keeps a good
Results 1  10
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455