Assessing Local Uncertainties with Geostatistical History Matching with Local Probability Distributions

2018 ◽  
Author(s):  
C. Marques ◽  
L. Azevedo ◽  
R. Nunes ◽  
A. Soares
Keyword(s):  
History Matching ◽  
Probability Distributions ◽  
Local Probability

scholarly journals Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example

2020 ◽  
Vol 8 ◽  
Author(s):  
Brioch Hemmings ◽  
Matthew J. Knowling ◽  
Catherine R. Moore
Keyword(s):  
Decision Support ◽  
Uncertainty Quantification ◽  
History Matching ◽  
Probability Distributions ◽  
Regional Scale ◽  
Model Performance ◽  
Relevant Information ◽  
Management Decision ◽  
Model Design

Effective decision making for resource management is often supported by combining predictive models with uncertainty analyses. This combination allows quantitative assessment of management strategy effectiveness and risk. Typically, history matching is undertaken to increase the reliability of model forecasts. However, the question of whether the potential benefit of history matching will be realized, or outweigh its cost, is seldom asked. History matching adds complexity to the modeling effort, as information from historical system observations must be appropriately blended with the prior characterization of the system. Consequently, the cost of history matching is often significant. When it is not implemented appropriately, history matching can corrupt model forecasts. Additionally, the available data may offer little decision-relevant information, particularly where data and forecasts are of different types, or represent very different stress regimes. In this paper, we present a decision support modeling workflow where early quantification of model uncertainty guides ongoing model design and deployment decisions. This includes providing justification for undertaking (or forgoing) history matching, so that unnecessary modeling costs can be avoided and model performance can be improved. The workflow is demonstrated using a regional-scale modeling case study in the Wairarapa Valley (New Zealand), where assessments of stream depletion and nitrate-nitrogen contamination risks are used to support water-use and land-use management decisions. The probability of management success/failure is assessed by comparing the proximity of model forecast probability distributions to ecologically motivated decision thresholds. This study highlights several important insights that can be gained by undertaking early uncertainty quantification, including: i) validation of the prior numerical characterization of the system, in terms of its consistency with historical observations; ii) validation of model design or indication of areas of model shortcomings; iii) evaluation of the relative proximity of management decision thresholds to forecast probability distributions, providing a justifiable basis for stopping modeling.

scholarly journals OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION

2013 ◽  
Vol 12 (08) ◽  
pp. 1341014 ◽  
Author(s):  
ROBERT J. PETRELLA
Keyword(s):  
Structure Prediction ◽  
Probability Distributions ◽  
Energy Shift ◽  
Model Systems ◽  
Local Probability ◽  
Optimal Accuracy ◽  
Simple Systems ◽  
Local Sampling ◽  
Correction Terms ◽  
Unequal Sampling

Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.

Image Labeling by Integration of Local Co-Occurrence Histogram and Global Features

2014 ◽  
Vol 18 (4) ◽  
pp. 511-517
Author(s):  
Takuto Omiya ◽  
◽  
Kazuhiro Hotta
Keyword(s):  
Posterior Probability ◽  
Probability Distributions ◽  
Texture Features ◽  
Bag Of Words ◽  
Global Features ◽  
Image Labeling ◽  
Local Probability ◽  
Local And Global Features ◽  
Conventional Methods ◽  
Labeling Method

In this paper, we perform image labeling based on the probabilistic integration of local and global features. Several conventional methods label pixels or regions using features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on local viewpoints. To overcome this problem, we propose an image labeling method that utilizes both local and global features. We compute the posterior probability distributions of the local and global features independently, and they are integrated by the product. To compute the probability of the global region (entire image), Bag-of-Words is used. In contrast, local cooccurrence between color and texture features is used to compute local probability. In the experiments, we use the MSRC21 dataset. The result demonstrates that the use of global viewpoint significantly improves labeling accuracy.

Probabilistische Zustandsanalyse von tragendem Mauerwerk / A Probabilistic Evaluation Method for Structural Masonry

1997 ◽  
Vol 3 (6) ◽  
pp. 553-568
Author(s):  
L. Schueremans ◽  
D. Van Gemert ◽  
J. Van Dyck
Keyword(s):  
Evaluation Method ◽  
Failure Modes ◽  
Probability Distributions ◽  
Probabilistic Method ◽  
Limit State ◽  
Evaluation Instrument ◽  
Visual Evaluation ◽  
Probabilistic Evaluation ◽  
Local Probability ◽  
State Functions

Abstract A probabilistic method to evaluate the reliability of structural masonry elements is presented. Local probability of failure, different failure modes and corresponding limit state functions, probability distributions of basic variables and model uncertainty are discussed. A graphical probability mapping is presented as an easilyaccessible, visual evaluation instrument in the restoration decision process. The proposed methodology is illustrated on tested shear panels, reported in literature.

Asteroid and Comet Impact Speeds upon Mars: Significance for Panspermia and the Supply of Organics

1997 ◽  
Vol 161 ◽  
pp. 197-201 ◽  
Author(s):  
Duncan Steel
Keyword(s):  
Probability Distributions ◽  
Regions Of Interest ◽  
Significant Fraction ◽  
Organic Chemicals ◽  
Impact Speed ◽  
The Mean ◽  
The Impact

AbstractWhilst lithopanspermia depends upon massive impacts occurring at a speed above some limit, the intact delivery of organic chemicals or other volatiles to a planet requires the impact speed to be below some other limit such that a significant fraction of that material escapes destruction. Thus the two opposite ends of the impact speed distributions are the regions of interest in the bioastronomical context, whereas much modelling work on impacts delivers, or makes use of, only the mean speed. Here the probability distributions of impact speeds upon Mars are calculated for (i) the orbital distribution of known asteroids; and (ii) the expected distribution of near-parabolic cometary orbits. It is found that cometary impacts are far more likely to eject rocks from Mars (over 99 percent of the cometary impacts are at speeds above 20 km/sec, but at most 5 percent of the asteroidal impacts); paradoxically, the objects impacting at speeds low enough to make organic/volatile survival possible (the asteroids) are those which are depleted in such species.

An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

2020 ◽  
Vol 3 (1) ◽  
pp. 10501-1-10501-9
Author(s):  
Christopher W. Tyler
Keyword(s):  
Belief Propagation ◽  
Probability Distributions ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Fusion Model ◽  
Cue Combination ◽  
Depth Estimate ◽  
Appropriate Behavior ◽  
Depth Cues ◽  
Bayesian Averaging

Abstract For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the “belief propagation” algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or “modified weak fusion” model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.

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