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11
Dependency networks for inference, collaborative filtering, and data visualization
 Journal of Machine Learning Research
"... We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set of ..."
Abstract

Cited by 157 (10 self)
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We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set of conditional distributions, one for each nodegiven its parents. We identify several basic properties of this representation and describe a computationally e cient procedure for learning the graph and probability components from data. We describe the application of this representation to probabilistic inference, collaborative ltering (the task of predicting preferences), and the visualization of acausal predictive relationships.
Markov Random Fields in Statistics
, 1990
"... For nearly a century, statisticians have been intrigued by the problems of developing a satisfactory methodology for the analysis of spatial data; see Student (1914), for an early example. It is only since the early 1970's, however, that the statistical analysis of large data sets, using flexible pa ..."
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Cited by 41 (0 self)
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For nearly a century, statisticians have been intrigued by the problems of developing a satisfactory methodology for the analysis of spatial data; see Student (1914), for an early example. It is only since the early 1970's, however, that the statistical analysis of large data sets, using flexible parametric models has become a feasible proposition. On the practical side, progress has been made possible by the availability of relatively cheap, computerised resources for the collection and analysis of data. The study of digital images and the use of satellite data for remote sensing are prominent examples in this respect. On the methodological side, substantial progress is associated with the introduction of Markov random fields (MRFs), as a class of parametric models for spatial data (Besag 1974). Shaped by these developments, spatial statistics has emerged as perhaps the most dynamic and computer intensive of all the areas of statistical endeavour; building upon models used originally...
A recursive algorithm for Markov random fields
 Biometrika
, 2002
"... The NRCSE was established in 1997 through a cooperative agreement with the United States Environmental Protection Agency which provides the Center's primary funding. A recursive algorithm for Markov random fields ..."
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Cited by 12 (1 self)
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The NRCSE was established in 1997 through a cooperative agreement with the United States Environmental Protection Agency which provides the Center's primary funding. A recursive algorithm for Markov random fields
Interpretation and Generalization of Score Matching
 In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence
, 2009
"... Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a forma ..."
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Cited by 8 (1 self)
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Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maximum likelihood and score matching. Our analysis shows that score matching finds model parameters that are more robust with noisy training data. Second, we develop a generalization of score matching. Based on this generalization, we further demonstrate an extension of score matching to models of discrete data. 1
A Statistical Perspective on Data Mining
"... Technological advances have led to new and automated data collection methods. Datasets once at a premium are often plentiful nowadays and sometimes indeed massive. A new breed of challenges are thus presented – primary among them is the need for methodology to analyze such masses of data with a view ..."
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Cited by 1 (0 self)
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Technological advances have led to new and automated data collection methods. Datasets once at a premium are often plentiful nowadays and sometimes indeed massive. A new breed of challenges are thus presented – primary among them is the need for methodology to analyze such masses of data with a view to understanding complex phenomena and relationships. Such capability is provided by data mining which combines core statistical techniques with those from machine intelligence. This article reviews the current state of the discipline from a statistician’s perspective, illustrates issues with reallife examples, discusses the connections with statistics, the differences, the failings and the challenges ahead. 1
AUTOLOGISTIC MODELS FOR BINARY DATA ON A LATTICE
"... Key words and phrases. Bayesian, Markov random field, maximum likelihood, ..."
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Cited by 1 (0 self)
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Key words and phrases. Bayesian, Markov random field, maximum likelihood,
Unifying NonMaximum Likelihood Learning Objectives with Minimum KL Contraction
"... When used to learn high dimensional parametric probabilistic models, the classical maximum likelihood (ML) learning often suffers from computational intractability, which motivates the active developments of nonML learning methods. Yet, because of their divergent motivations and forms, the objectiv ..."
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When used to learn high dimensional parametric probabilistic models, the classical maximum likelihood (ML) learning often suffers from computational intractability, which motivates the active developments of nonML learning methods. Yet, because of their divergent motivations and forms, the objective functions of many nonML learning methods are seemingly unrelated, and there lacks a unified framework to understand them. In this work, based on an information geometric view of parametric learning, we introduce a general nonML learning principle termed as minimum KL contraction, where we seek optimal parameters that minimizes the contraction of the KL divergence between the two distributions after they are transformed with a KL contraction operator. We then show that the objective functions of several important or recently developed nonML learning methods, including contrastive divergence [12], noisecontrastive estimation [11], partial likelihood [7], nonlocal contrastive objectives [31], score matching [14], pseudolikelihood [3], maximum conditional likelihood [17], maximum mutual information [2], maximum marginal likelihood [9], and conditional and marginal composite likelihood [24], can be unified under the minimum KL contraction framework with different choices of the KL contraction operators. 1
Economic dynamics with financial fragility and meanfield interaction: a model
, 709
"... Following the statistical mechanics methodology, firstly introduced in macroeconomics by Aoki [1, 2, 3], we provide some insights to the well known works of (author?) [7, 6]. Specifically, we reach analytically a closed form solution of their models overcoming the aggregation problem. The key idea i ..."
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Following the statistical mechanics methodology, firstly introduced in macroeconomics by Aoki [1, 2, 3], we provide some insights to the well known works of (author?) [7, 6]. Specifically, we reach analytically a closed form solution of their models overcoming the aggregation problem. The key idea is to represent the economy as an evolving complex system, composed by heterogeneous interacting agents, that can partitioned into a space of macroscopic states. This meso level of aggregation permits to adopt mean field interaction modeling and master equation techniques.
of LaborThe Effect of Migration and Spatial Connectivity on Regional Skill Endowments across Europe: 19882010
, 2013
"... Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The ..."
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Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be
13. Financial fragility, meanfield interaction and macroeconomic dynamics: a stochastic model
"... In recent decades, a considerable stream of research, following the complexity approach (Rosser, 2004), has developed a series of models that import concepts and tools from hard sciences to economics. They represent an attempt to identify an alternative framework to the representative agent ..."
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In recent decades, a considerable stream of research, following the complexity approach (Rosser, 2004), has developed a series of models that import concepts and tools from hard sciences to economics. They represent an attempt to identify an alternative framework to the representative agent