The Experts below are selected from a list of 99522 Experts worldwide ranked by ideXlab platform
Haifeng Chen - One of the best experts on this subject based on the ideXlab platform.
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time series segmentation to discover behavior switching in Complex Physical Systems
International Conference on Data Mining, 2015Co-Authors: Zheng Han, Haifeng Chen, Tan Yan, Geoff JiangAbstract:An accurate and automated identification of operational behavior switching is critical to the autonomic management of Complex Systems. In this paper, we collect sensor readings from those Systems, which are treated as time series, and propose a solution to discover switching behaviors by inferring the relationship changes among massive time series. The method first learns a sequence of local relationship models that can best fit the time series data, and then combines the changes of local relationships to identify the system level behavior switching. In the local relationship modeling, we formulate the underlying switching identification as a segmentation problem, and propose a sophisticated optimization algorithm to accurately discover different segments in time series. In addition, we develop a hierarchical optimization strategy to further improve the efficiency of segmentation. To unveil the system level behavior switching, we present a density estimation and mode search algorithm to effectively aggregate the segmented local relationships so that the global switch points can be captured. Our method has been evaluated on both synthetic data and datasets from real Systems. Experimental results demonstrate that it can successfully discover behavior switching in different Systems.
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efficient long term degradation profiling in time series for Complex Physical Systems
Knowledge Discovery and Data Mining, 2015Co-Authors: Liudmila Ulanova, Haifeng Chen, Tan Yan, Guofei Jiang, Eamonn Keogh, Kai ZhangAbstract:The long term operation of Physical Systems inevitably leads to their wearing out, and may cause degradations in performance or the unexpected failure of the entire system. To reduce the possibility of such unanticipated failures, the system must be monitored for tell-tale symptoms of degradation that are suggestive of imminent failure. In this work, we introduce a novel time series analysis technique that allows the decomposition of the time series into trend and fluctuation components, providing the monitoring software with actionable information about the changes of the system's behavior over time. We analyze the underlying problem and formulate it to a Quadratic Programming (QP) problem that can be solved with existing QP-solvers. However, when the profiling resolution is high, as generally required by real-world applications, such a decomposition becomes intractable to general QP-solvers. To speed up the problem solving, we further transform the problem and present a novel QP formulation, Non-negative QP, for the problem and demonstrate a tractable solution that bypasses the use of slow general QP-solvers. We demonstrate our ideas on both synthetic and real datasets, showing that our method allows us to accurately extract the degradation phenomenon of time series. We further demonstrate the generality of our ideas by applying them beyond classic machine prognostics to problems in identifying the influence of news events on currency exchange rates and stock prices. We fully implement our profiling system and deploy it into several Physical Systems, such as chemical plants and nuclear power plants, and it greatly helps detect the degradation phenomenon, and diagnose the corresponding components.
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KDD - Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015Co-Authors: Liudmila Ulanova, Haifeng Chen, Tan Yan, Guofei Jiang, Eamonn Keogh, Kai ZhangAbstract:The long term operation of Physical Systems inevitably leads to their wearing out, and may cause degradations in performance or the unexpected failure of the entire system. To reduce the possibility of such unanticipated failures, the system must be monitored for tell-tale symptoms of degradation that are suggestive of imminent failure. In this work, we introduce a novel time series analysis technique that allows the decomposition of the time series into trend and fluctuation components, providing the monitoring software with actionable information about the changes of the system's behavior over time. We analyze the underlying problem and formulate it to a Quadratic Programming (QP) problem that can be solved with existing QP-solvers. However, when the profiling resolution is high, as generally required by real-world applications, such a decomposition becomes intractable to general QP-solvers. To speed up the problem solving, we further transform the problem and present a novel QP formulation, Non-negative QP, for the problem and demonstrate a tractable solution that bypasses the use of slow general QP-solvers. We demonstrate our ideas on both synthetic and real datasets, showing that our method allows us to accurately extract the degradation phenomenon of time series. We further demonstrate the generality of our ideas by applying them beyond classic machine prognostics to problems in identifying the influence of news events on currency exchange rates and stock prices. We fully implement our profiling system and deploy it into several Physical Systems, such as chemical plants and nuclear power plants, and it greatly helps detect the degradation phenomenon, and diagnose the corresponding components.
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a quality control engine for Complex Physical Systems
Dependable Systems and Networks, 2015Co-Authors: Haifeng Chen, Mizoguchi Takehiko, Yan Tan, Kai Zhang, Geoff JiangAbstract:This paper proposes a novel framework to automatically pinpoint suspicious sensors that lead to the quality change in Physical Systems such as manufacture plants. Our framework treats sensor readings as time series, and contains three main stages: time series transformation to feature series, feature ranking, and ranking score fusion. In the first step, we transform time series into a number of different feature series to describe the underlying dynamics of each sensor data. After that, the importance scores of all feature series are computed by utilizing several feature selection and ranking techniques, each of which discovers specific aspects of feature importance and their dependencies in the feature space. Finally we combine importance scores from all the rankers and all the features to obtain the final ranking of each sensor with respect to the system quality change. Our experiments based on synthetic time series as well as sensor data from a real system demonstrate the effectiveness of proposed method. In addition, we have implemented our framework as a production engine, and successfully applied it to several real Physical Systems.
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ICDM - Time Series Segmentation to Discover Behavior Switching in Complex Physical Systems
2015 IEEE International Conference on Data Mining, 2015Co-Authors: Zheng Han, Haifeng Chen, Tan Yan, Geoff JiangAbstract:An accurate and automated identification of operational behavior switching is critical to the autonomic management of Complex Systems. In this paper, we collect sensor readings from those Systems, which are treated as time series, and propose a solution to discover switching behaviors by inferring the relationship changes among massive time series. The method first learns a sequence of local relationship models that can best fit the time series data, and then combines the changes of local relationships to identify the system level behavior switching. In the local relationship modeling, we formulate the underlying switching identification as a segmentation problem, and propose a sophisticated optimization algorithm to accurately discover different segments in time series. In addition, we develop a hierarchical optimization strategy to further improve the efficiency of segmentation. To unveil the system level behavior switching, we present a density estimation and mode search algorithm to effectively aggregate the segmented local relationships so that the global switch points can be captured. Our method has been evaluated on both synthetic data and datasets from real Systems. Experimental results demonstrate that it can successfully discover behavior switching in different Systems.
Zohar Nussinov - One of the best experts on this subject based on the ideXlab platform.
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Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering
Information Science for Materials Discovery and Design, 2016Co-Authors: Zohar Nussinov, P. Ronhovde, Nicholas A. Mauro, Shaibal Chakrabarty, Dandan Hu, Bo Sun, K K SahuAbstract:We survey the application of a relatively new branch of statistical physics—“community detectionCommunity detection”—to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a Complex systemComplex system involving many elements into optimally decoupled subsets or communities of such elements. We review a multiresolution variant which is used to ascertain structures at different spatial and temporal scales. Significant patterns are obtained by examining the correlations between different independent solvers. Similar to other combinatorial optimization problems in the NP Complexity class, community detection exhibits several phases. Typically, illuminating orders are revealed by choosing parameters that lead to extremal information theory correlationsInformation theory correlations.
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Inference of hidden structures in Complex Physical Systems by multi-scale clustering
Information Science for Materials Discovery and Design, 2015Co-Authors: Zohar Nussinov, P. Ronhovde, Bo Sun, Saurish Chakrabarty, M. Sahu, N. A. Mauro, K K SahuAbstract:We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a Complex system involving many elements into optimally decoupled subsets or communities of such elements. We review a multiresolution variant which is used to ascertain structures at different spatial and temporal scales. Significant patterns are obtained by examining the correlations between different independent solvers. Similar to other combinatorial optimization problems in the NP Complexity class, community detection exhibits several phases. Typically, illuminating orders are revealed by choosing parameters that lead to extremal information theory correlations.
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Detection of hidden structures for arbitrary scales in Complex Physical Systems.
Scientific reports, 2012Co-Authors: P. Ronhovde, K K Sahu, Saurish Chakrabarty, M. Sahu, K. F. Kelton, N. A. Mauro, Zohar NussinovAbstract:Recent decades have experienced the discovery of numerous Complex materials. At the root of the Complexity underlying many of these materials lies a large number of contending atomic- and largerscale configurations. In order to obtain a more detailed understanding of such Systems, we need tools that enable the detection of pertinent structures on all spatial and temporal scales. Towards this end, we suggest a new method that applies to both static and dynamic Systems which invokes ideas from network analysis and information theory. Our approach efficiently identifies basic unit cells, topological defects and candidate natural structures. The method is particularly useful where a clear definition of order is lacking and the identified features may constitute a natural point of departure for further analysis.
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Detecting hidden spatial and spatio-temporal structures in glasses and Complex Physical Systems by multiresolution network clustering
The European physical journal. E Soft matter, 2011Co-Authors: P. Ronhovde, Nicholas A. Mauro, K K Sahu, Saurish Chakrabarty, M. Sahu, K. F. Kelton, Zohar NussinovAbstract:We elaborate on a general method that we recently introduced for characterizing the “natural” structures in Complex Physical Systems via multi-scale network analysis. The method is based on “community detection” wherein interacting particles are partitioned into an “ideal gas” of optimally decoupled groups of particles. Specifically, we construct a set of network representations (“replicas”) of the Physical system based on interatomic potentials and apply a multiscale clustering (“multiresolution community detection”) analysis using information-based correlations among the replicas. Replicas may i) be different representations of an identical static system, ii) embody dynamics by considering replicas to be time separated snapshots of the system (with a tunable time separation), or iii) encode general correlations when different replicas correspond to different representations of the entire history of the system as it evolves in space-time. Inputs for our method are the inter-particle potentials or experimentally measured two (or higher order) particle correlations. We apply our method to computer simulations of a binary Kob-Andersen Lennard-Jones system in a mixture ratio of A80B20 , a ternary model system with components “A”, “B”, and “C” in ratios of A88B7C5 (as in Al88Y7Fe5 , and to atomic coordinates in a Zr80Pt20 system as gleaned by reverse Monte Carlo analysis of experimentally determined structure factors. We identify the dominant structures (disjoint or overlapping) and general length scales by analyzing extrema of the information theory measures. We speculate on possible links between i) Physical transitions or crossovers and ii) changes in structures found by this method as well as phase transitions associated with the computational Complexity of the community detection problem. We also briefly consider continuum approaches and discuss rigidity and the shear penetration depth in amorphous Systems; this latter length scale increases as the system becomes progressively rigid.
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Detecting hidden spatial and spatio-temporal structures in glasses and Complex Physical Systems by multiresolution network clustering
arXiv: Materials Science, 2011Co-Authors: P. Ronhovde, Nicholas A. Mauro, K K Sahu, Saurish Chakrabarty, M. Sahu, K. F. Kelton, Zohar NussinovAbstract:We elaborate on a general method that we recently introduced for characterizing the "natural" structures in Complex Physical Systems via a multiscale network based approach for the data mining of such structures. The approach is based on "community detection" wherein interacting particles are partitioned into "an ideal gas" of optimally decoupled groups of particles. Specifically, we construct a set of network representations ("replicas") of the Physical system based on interatomic potentials and apply a multiscale clustering ("multiresolution community detection") analysis using information-based correlations among the replicas. Replicas may be (i) different representations of an identical static system or (ii) embody dynamics by when considering replicas to be time separated snapshots of the system (with a tunable time separation) or (iii) encode general correlations when different replicas correspond to different representations of the entire history of the system as it evolves in space-time. We apply our method to computer simulations of a binary Kob-Andersen Lennard-Jones system, a ternary model system, and to atomic coordinates in a ZrPt system as gleaned by reverse Monte Carlo analysis of experimentally determined structure factors. We identify the dominant structures (disjoint or overlapping) and general length scales by analyzing extrema of the information theory measures. We speculate on possible links between (i) Physical transitions or crossovers and (ii) changes in structures found by this method as well as phase transitions associated with the computational Complexity of the community detection problem. We briefly also consider continuum approaches and discuss the shear penetration depth in elastic media; this length scale increases as the system becomes increasingly rigid.
Geoff Jiang - One of the best experts on this subject based on the ideXlab platform.
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time series segmentation to discover behavior switching in Complex Physical Systems
International Conference on Data Mining, 2015Co-Authors: Zheng Han, Haifeng Chen, Tan Yan, Geoff JiangAbstract:An accurate and automated identification of operational behavior switching is critical to the autonomic management of Complex Systems. In this paper, we collect sensor readings from those Systems, which are treated as time series, and propose a solution to discover switching behaviors by inferring the relationship changes among massive time series. The method first learns a sequence of local relationship models that can best fit the time series data, and then combines the changes of local relationships to identify the system level behavior switching. In the local relationship modeling, we formulate the underlying switching identification as a segmentation problem, and propose a sophisticated optimization algorithm to accurately discover different segments in time series. In addition, we develop a hierarchical optimization strategy to further improve the efficiency of segmentation. To unveil the system level behavior switching, we present a density estimation and mode search algorithm to effectively aggregate the segmented local relationships so that the global switch points can be captured. Our method has been evaluated on both synthetic data and datasets from real Systems. Experimental results demonstrate that it can successfully discover behavior switching in different Systems.
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a quality control engine for Complex Physical Systems
Dependable Systems and Networks, 2015Co-Authors: Haifeng Chen, Mizoguchi Takehiko, Yan Tan, Kai Zhang, Geoff JiangAbstract:This paper proposes a novel framework to automatically pinpoint suspicious sensors that lead to the quality change in Physical Systems such as manufacture plants. Our framework treats sensor readings as time series, and contains three main stages: time series transformation to feature series, feature ranking, and ranking score fusion. In the first step, we transform time series into a number of different feature series to describe the underlying dynamics of each sensor data. After that, the importance scores of all feature series are computed by utilizing several feature selection and ranking techniques, each of which discovers specific aspects of feature importance and their dependencies in the feature space. Finally we combine importance scores from all the rankers and all the features to obtain the final ranking of each sensor with respect to the system quality change. Our experiments based on synthetic time series as well as sensor data from a real system demonstrate the effectiveness of proposed method. In addition, we have implemented our framework as a production engine, and successfully applied it to several real Physical Systems.
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ICDM - Time Series Segmentation to Discover Behavior Switching in Complex Physical Systems
2015 IEEE International Conference on Data Mining, 2015Co-Authors: Zheng Han, Haifeng Chen, Tan Yan, Geoff JiangAbstract:An accurate and automated identification of operational behavior switching is critical to the autonomic management of Complex Systems. In this paper, we collect sensor readings from those Systems, which are treated as time series, and propose a solution to discover switching behaviors by inferring the relationship changes among massive time series. The method first learns a sequence of local relationship models that can best fit the time series data, and then combines the changes of local relationships to identify the system level behavior switching. In the local relationship modeling, we formulate the underlying switching identification as a segmentation problem, and propose a sophisticated optimization algorithm to accurately discover different segments in time series. In addition, we develop a hierarchical optimization strategy to further improve the efficiency of segmentation. To unveil the system level behavior switching, we present a density estimation and mode search algorithm to effectively aggregate the segmented local relationships so that the global switch points can be captured. Our method has been evaluated on both synthetic data and datasets from real Systems. Experimental results demonstrate that it can successfully discover behavior switching in different Systems.
Tan Yan - One of the best experts on this subject based on the ideXlab platform.
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time series segmentation to discover behavior switching in Complex Physical Systems
International Conference on Data Mining, 2015Co-Authors: Zheng Han, Haifeng Chen, Tan Yan, Geoff JiangAbstract:An accurate and automated identification of operational behavior switching is critical to the autonomic management of Complex Systems. In this paper, we collect sensor readings from those Systems, which are treated as time series, and propose a solution to discover switching behaviors by inferring the relationship changes among massive time series. The method first learns a sequence of local relationship models that can best fit the time series data, and then combines the changes of local relationships to identify the system level behavior switching. In the local relationship modeling, we formulate the underlying switching identification as a segmentation problem, and propose a sophisticated optimization algorithm to accurately discover different segments in time series. In addition, we develop a hierarchical optimization strategy to further improve the efficiency of segmentation. To unveil the system level behavior switching, we present a density estimation and mode search algorithm to effectively aggregate the segmented local relationships so that the global switch points can be captured. Our method has been evaluated on both synthetic data and datasets from real Systems. Experimental results demonstrate that it can successfully discover behavior switching in different Systems.
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efficient long term degradation profiling in time series for Complex Physical Systems
Knowledge Discovery and Data Mining, 2015Co-Authors: Liudmila Ulanova, Haifeng Chen, Tan Yan, Guofei Jiang, Eamonn Keogh, Kai ZhangAbstract:The long term operation of Physical Systems inevitably leads to their wearing out, and may cause degradations in performance or the unexpected failure of the entire system. To reduce the possibility of such unanticipated failures, the system must be monitored for tell-tale symptoms of degradation that are suggestive of imminent failure. In this work, we introduce a novel time series analysis technique that allows the decomposition of the time series into trend and fluctuation components, providing the monitoring software with actionable information about the changes of the system's behavior over time. We analyze the underlying problem and formulate it to a Quadratic Programming (QP) problem that can be solved with existing QP-solvers. However, when the profiling resolution is high, as generally required by real-world applications, such a decomposition becomes intractable to general QP-solvers. To speed up the problem solving, we further transform the problem and present a novel QP formulation, Non-negative QP, for the problem and demonstrate a tractable solution that bypasses the use of slow general QP-solvers. We demonstrate our ideas on both synthetic and real datasets, showing that our method allows us to accurately extract the degradation phenomenon of time series. We further demonstrate the generality of our ideas by applying them beyond classic machine prognostics to problems in identifying the influence of news events on currency exchange rates and stock prices. We fully implement our profiling system and deploy it into several Physical Systems, such as chemical plants and nuclear power plants, and it greatly helps detect the degradation phenomenon, and diagnose the corresponding components.
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KDD - Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015Co-Authors: Liudmila Ulanova, Haifeng Chen, Tan Yan, Guofei Jiang, Eamonn Keogh, Kai ZhangAbstract:The long term operation of Physical Systems inevitably leads to their wearing out, and may cause degradations in performance or the unexpected failure of the entire system. To reduce the possibility of such unanticipated failures, the system must be monitored for tell-tale symptoms of degradation that are suggestive of imminent failure. In this work, we introduce a novel time series analysis technique that allows the decomposition of the time series into trend and fluctuation components, providing the monitoring software with actionable information about the changes of the system's behavior over time. We analyze the underlying problem and formulate it to a Quadratic Programming (QP) problem that can be solved with existing QP-solvers. However, when the profiling resolution is high, as generally required by real-world applications, such a decomposition becomes intractable to general QP-solvers. To speed up the problem solving, we further transform the problem and present a novel QP formulation, Non-negative QP, for the problem and demonstrate a tractable solution that bypasses the use of slow general QP-solvers. We demonstrate our ideas on both synthetic and real datasets, showing that our method allows us to accurately extract the degradation phenomenon of time series. We further demonstrate the generality of our ideas by applying them beyond classic machine prognostics to problems in identifying the influence of news events on currency exchange rates and stock prices. We fully implement our profiling system and deploy it into several Physical Systems, such as chemical plants and nuclear power plants, and it greatly helps detect the degradation phenomenon, and diagnose the corresponding components.
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ICDM - Time Series Segmentation to Discover Behavior Switching in Complex Physical Systems
2015 IEEE International Conference on Data Mining, 2015Co-Authors: Zheng Han, Haifeng Chen, Tan Yan, Geoff JiangAbstract:An accurate and automated identification of operational behavior switching is critical to the autonomic management of Complex Systems. In this paper, we collect sensor readings from those Systems, which are treated as time series, and propose a solution to discover switching behaviors by inferring the relationship changes among massive time series. The method first learns a sequence of local relationship models that can best fit the time series data, and then combines the changes of local relationships to identify the system level behavior switching. In the local relationship modeling, we formulate the underlying switching identification as a segmentation problem, and propose a sophisticated optimization algorithm to accurately discover different segments in time series. In addition, we develop a hierarchical optimization strategy to further improve the efficiency of segmentation. To unveil the system level behavior switching, we present a density estimation and mode search algorithm to effectively aggregate the segmented local relationships so that the global switch points can be captured. Our method has been evaluated on both synthetic data and datasets from real Systems. Experimental results demonstrate that it can successfully discover behavior switching in different Systems.
Michael Goldstein - One of the best experts on this subject based on the ideXlab platform.
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Bayesian uncertainty analysis for Complex Physical Systems modelled by computer simulators with applications to tipping points.
Communications in Nonlinear Science and Numerical Simulation, 2015Co-Authors: Camila C. S. Caiado, Michael GoldsteinAbstract:In this paper we present and illustrate basic Bayesian techniques for the uncertainty analysis of Complex Physical Systems modelled by computer simulators. We focus on emulation and history matching and also discuss the treatment of observational errors and structural discrepancies in time series. We exemplify such methods using a four-box model for the termohaline circulation. We show how these methods may be applied to Systems containing tipping points and how to treat possible discontinuities using multiple emulators.
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Environmental Modelling: Finding Simplicity in Complexity, Second Edition - Assessing Model Adequacy
Environmental Modelling, 2013Co-Authors: Michael Goldstein, Allan H. Seheult, Ian VernonAbstract:Environmental models are simplified representations of Complex Physical Systems. The implementation of any such model, as a computer simulator, involves further simplifications and approximations. The value of the resulting simulator, in giving scientific and practical insights into the functioning of the corresponding Physical system, depends both on the nature and degree of these simplifications and also on the objectives for which the model is to be used.
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WoCoUQ - Bayes Linear Analysis for Complex Physical Systems Modeled by Computer Simulators
IFIP Advances in Information and Communication Technology, 2012Co-Authors: Michael GoldsteinAbstract:Most large and Complex Physical Systems are studied by mathematical models, implemented as high dimensional computer simulators. While all such cases differ in Physical description, each analysis of a Physical system based on a computer simulator involves the same underlying sources of uncertainty. These sources are defined and described below. In addition, there is a growing field of study which aims to quantify and synthesize all of the uncertainties involved in relating models to Physical Systems, within the framework of Bayesian statistics, and to use the resultant uncertainty specification to address problems of forecasting and decision making based on the application of these methods. We present an overview of the current status and future challenges in this emerging methodology, illustrating with examples drawn from current areas of application including: asset management for oil reservoirs, galaxy modeling, and rapid climate change.
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Bayes Linear Analysis for Complex Physical Systems Modeled by Computer Simulators
2011Co-Authors: Michael GoldsteinAbstract:Most large and Complex Physical Systems are studied by mathematical models, implemented as high dimensional computer simulators. While all such cases differ in Physical description, each analysis of a Physical system based on a computer simulator involves the same underlying sources of uncertainty. These sources are defined and described below. In addition, there is a growing field of study which aims to quantify and synthesize all of the uncertainties involved in relating models to Physical Systems, within the framework of Bayesian statistics, and to use the resultant uncertainty specification to address problems of forecasting and decision making based on the application of these methods. We present an overview of the current status and future challenges in this emerging methodology, illustrating with examples drawn from current areas of application including: asset management for oil reservoirs, galaxy modeling, and rapid climate change.
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Constructing partial prior specifications for models of Complex Physical Systems
Journal of the Royal Statistical Society: Series D (The Statistician), 1998Co-Authors: Peter S. Craig, Michael Goldstein, Allan H. Seheult, James SmithAbstract:Summary. Many large scale problems, particularly in the Physical sciences, are solved using Complex, high dimensional models whose outputs, for a given set of inputs, are expensive and time consuming to evaluate. The Complexity of such problems forces us to focus attention on those limited aspects of uncertainty which are directly relevant to the tasks for which the model will be used. We discuss methods for constructing the relevant partial prior specifications for these uncertainties, based on the prior covariance structure. Our approach combines two sources of prior knowledge. First, we elicit both qualitative and quantitative prior information based on expert prior judgments, using computer-based elicitation tools for organizing the Complex collection of assessments in a systematic way. Secondly, we test and refine these judgments using detailed experiments based on versions of the model which are cheaper to evaluate. Although the approach is quite general, we illustrate it in the context of matching hydrocarbon reservoir history.