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289
Operations for Learning with Graphical Models
 Journal of Artificial Intelligence Research
, 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
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Cited by 249 (12 self)
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This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feedforward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
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Cited by 172 (0 self)
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This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
Measuring Expectations
, 2004
"... This article discusses the history underlying the new literature, describes some of what has been learned thus far, and looks ahead towards making further progress ..."
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Cited by 128 (8 self)
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This article discusses the history underlying the new literature, describes some of what has been learned thus far, and looks ahead towards making further progress
Using expectations data to study subjective income expectations
 Journal of the American Statistical Association
, 1997
"... We have collected data on the oneyearahead income expectations of members of American households in our Survey of Economic Expectations (SEE), a module of a national continuous telephone survey conducted at the University of Wisconsin. The incomeexpectations questions take this form: "What do you ..."
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Cited by 77 (11 self)
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We have collected data on the oneyearahead income expectations of members of American households in our Survey of Economic Expectations (SEE), a module of a national continuous telephone survey conducted at the University of Wisconsin. The incomeexpectations questions take this form: "What do you think is the percent chance (or what are the chances out of 100) that your total household income, before taxes, will be less than Y over the next 12 months? " We use the responses to a sequence of such questions posed for different income thresholds Y to estimate each respondent's subjective probability distribution for next year's household income. We use the estimates to study the crosssectional variation in income expectations one year into the future
Uncertainty analysis of climate change and policy response
 Climatic Change
, 2003
"... Abstract. To aid climate policy decisions, accurate quantitative descriptions of the uncertainty in climate outcomes under various possible policies are needed. Here, we apply an earth systems model to describe the uncertainty in climate projections under two different policy scenarios. This study i ..."
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Cited by 51 (12 self)
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Abstract. To aid climate policy decisions, accurate quantitative descriptions of the uncertainty in climate outcomes under various possible policies are needed. Here, we apply an earth systems model to describe the uncertainty in climate projections under two different policy scenarios. This study illustrates an internally consistent uncertainty analysis of one climate assessment modeling framework, propagating uncertainties in both economic and climate components, and constraining climate parameter uncertainties based on observation. We find that in the absence of greenhouse gas emissions restrictions, there is a one in forty chance that global mean surface temperature change will exceed 4.9 ◦C by the year 2100. A policy case with aggressive emissions reductions over time lowers the temperature change to a one in forty chance of exceeding 3.2 ◦C, thus reducing but not eliminating the chance of substantial warming. 1.
How Much Is Enough? A RiskManagement Approach to Computer Security
"... How much security is enough? No one today can satisfactorily answer this question for computerrelated risks. The first generation of computer security risk modelers struggled with issues arising out of their binary view of security, ensnaring them in an endless web of assessment, disagreement, and ..."
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Cited by 44 (0 self)
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How much security is enough? No one today can satisfactorily answer this question for computerrelated risks. The first generation of computer security risk modelers struggled with issues arising out of their binary view of security, ensnaring them in an endless web of assessment, disagreement, and gridlock. Even as professional risk managers wrest responsibility away from the firstgeneration technologists, they are still unable to answer the question with sufficient quantitative rigor. Their efforts are handicapped by a reliance on nonquantitative methodologies originally developed to address the deployment and organizational acceptance issues that plagued firstgeneration tools.
Probabilities for a Probabilistic Network: A CaseStudy in Oesophageal Carcinoma
 ARTIFICIAL INTELLIGENCE IN MEDICINE
, 2001
"... With the help of two experts in gastrointestinal oncology from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decisionsupport system is being developed for patientspecific therapy selection for oesophageal carcinoma. The kernel of the system is a probabilistic network that desc ..."
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Cited by 43 (11 self)
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With the help of two experts in gastrointestinal oncology from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decisionsupport system is being developed for patientspecific therapy selection for oesophageal carcinoma. The kernel of the system is a probabilistic network that describes the characteristics of oesophageal carcinoma and the pathophysiological processes of invasion and metastasis. While the construction of the graphical structure of the network was relatively straightforward, probability elicitation with existing methods proved to be a major obstacle. We designed
Climate Change Policy: Quantifying Uncertainties for Damages and Optimal Carbon Taxes.” Energy Policy 27: 415429. 23 Sidgwick, Henry 1890. The Methods of Ethics
, 1999
"... Controversy surrounds climate change policy analyses because of uncertainties in climatic effects, impacts, mitigation costs and their distributions. Here ..."
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Cited by 38 (4 self)
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Controversy surrounds climate change policy analyses because of uncertainties in climatic effects, impacts, mitigation costs and their distributions. Here
Active Nonlinear Tests (ANTs) of Complex Simulation Models
 Tournament Selection and the Effects of Noise”, Complex Systems 9
, 1996
"... Simulation models are becoming increasingly common in the analysis of critical scientific, policy, and management issues. Such models provide a way to analyze complex systems characterized by both large parameter spaces and nonlinear interactions. Unfortunately, these same characteristics make under ..."
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Cited by 35 (0 self)
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Simulation models are becoming increasingly common in the analysis of critical scientific, policy, and management issues. Such models provide a way to analyze complex systems characterized by both large parameter spaces and nonlinear interactions. Unfortunately, these same characteristics make understanding such models using traditional testing techniques extremely difficult. Here we show how a model's structure and robustness can be tested via a simple, automatic, nonlinear search algorithm designed to actively "break" the model's implications. Using the active nonlinear tests (ANTs) developed here, one can easily probe for key weaknesses in a simulation's structure, and thereby begin to improve and refine the model's design. We demonstrate ANTs by testing a wellknown model of global dynamics (World3), and show how this technique can be used to uncover small, but powerful, nonlinear effects that may highlight vulnerabilities in the original model. This paper has benefited from disc...
How to Elicit Many Probabilities
 Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
, 1999
"... In building Bayesian belief networks, the elicitation of all probabilities required can be a major obstacle. We learned the extent of this oftencited observation in the construction of the probabilistic part of a complex influence diagram in the field of cancer treatment. Based upon our negative ex ..."
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Cited by 33 (7 self)
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In building Bayesian belief networks, the elicitation of all probabilities required can be a major obstacle. We learned the extent of this oftencited observation in the construction of the probabilistic part of a complex influence diagram in the field of cancer treatment. Based upon our negative experiences with existing methods, we designed a new method for probability elicitation from domain experts. The method combines various ideas, among which are the ideas of transcribing probabilities and of using a scale with both numerical and verbal anchors for marking assessments. In the construction of the probabilistic part of our influence diagram, the method proved to allow for the elicitation of many probabilities in little time.