A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation

2005 ◽  
pp. 600-611 ◽  
Author(s):  
Min Zhang ◽  
Haizhou Li ◽  
Jian Su ◽  
Hendra Setiawan
Keyword(s):  
Probability Model ◽  
Joint Probability ◽  
Named Entity ◽  
Context Dependent ◽  
Joint Probability Model

3-Dimensional direct sampling-based environmental contours using a semi-parametric joint probability model

2021 ◽  
Vol 112 ◽  
pp. 102710
Author(s):  
Xiaoyu Bai ◽  
Hui Jiang ◽  
Xiaoyu Huang ◽  
Guangsong Song ◽  
Xinyi Ma
Keyword(s):  
Probability Model ◽  
Joint Probability ◽  
3 Dimensional ◽  
Direct Sampling ◽  
Joint Probability Model

Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model

2015 ◽  
Vol 528 ◽  
pp. 329-340 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Q.J. Wang ◽  
James C. Bennett ◽  
David E. Robertson ◽  
Quanxi Shao ◽  
...  
Keyword(s):  
Probability Model ◽  
Joint Probability ◽  
Predictive Uncertainty ◽  
Streamflow Forecasts ◽  
Joint Probability Model

scholarly journals A Novel Method of Seismic Signal Detection Using Waveform Features

2020 ◽  
Vol 10 (8) ◽  
pp. 2919
Author(s):  
Jian Li ◽  
Mengmin He ◽  
Gaofeng Cui ◽  
Xiaoming Wang ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Signal Detection ◽  
Probability Model ◽  
Joint Probability ◽  
Seismic Signal ◽  
Detection Performance ◽  
Feature Model ◽  
Location Information ◽  
Seismic Signals ◽  
Historical Events ◽  
Joint Probability Model

The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.

scholarly journals A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts

2019 ◽  
Vol 148 (1) ◽  
pp. 241-257 ◽  
Author(s):  
Wentao Li ◽  
Quan J. Wang ◽  
Qingyun Duan
Keyword(s):  
Correlation Coefficient ◽  
Extreme Events ◽  
Prediction Models ◽  
Probability Model ◽  
Weather Prediction ◽  
Joint Probability ◽  
Correlation Model ◽  
Asymmetric Dependence ◽  
Huai River ◽  
Joint Probability Model

Abstract Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficient in the traditional joint probability model lacks the flexibility to model asymmetric dependence. In this study, we formulated a new postprocessing model with a decreasing correlation coefficient to characterize asymmetric dependence. We carried out experiments using Global Ensemble Forecast System reforecasts for daily precipitation in the Huai River basin in China. The results show that, although it performs well in terms of continuous ranked probability score or reliability for all events, the traditional joint probability model suffers from overestimation for extreme events defined by the largest 2.5% or 5% of raw forecasts. On the contrary, the proposed variable-correlation model is able to alleviate the overestimation and achieves better reliability for extreme events than the traditional model. The proposed variable-correlation model can be seen as a flexible extension of the traditional joint probability model to improve the performance for extreme events.

A long-term joint probability model for metocean circular and linear characteristics

2018 ◽  
Vol 75 ◽  
pp. 143-152 ◽  
Author(s):  
Zohreh Sadat Haghayeghi ◽  
Mohammad Javad Ketabdari
Keyword(s):  
Probability Model ◽  
Joint Probability ◽  
Joint Probability Model

scholarly journals Environmental Economics and Uncertainty: Review and a Machine Learning Outlook

2020 ◽  
Author(s):  
Ruda Zhang ◽  
Patrick Wingo ◽  
Rodrigo Duran ◽  
Kelly Rose ◽  
Jennifer Bauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Environmental Economics ◽  
Latent Variables ◽  
Integrated Assessment ◽  
Probability Model ◽  
Joint Probability ◽  
Estimation Methods ◽  
Environmental Models ◽  
Manifold Sampling ◽  
Joint Probability Model

Economic assessment in environmental science means measuring and evaluating environmental impacts, adaptation, and vulnerability. Integrated assessment modeling (IAM) is a unifying framework of environmental economics, which attempts to combine key elements of physical, ecological, and socioeconomic systems. The first part of this article reviews the literature on the IAM framework: its components, relations between the components, and examples. For such models to inform environmental decision-making, they must quantify the uncertainties associated with their estimates. Uncertainty characterization in integrated assessment varies by component models: uncertainties associated with mechanistic physical models are often assessed with an ensemble of simulations or Monte Carlo sampling, while uncertainties associated with impact models are evaluated by conjecture or econometric analysis. The second part of this article reviews the literature on uncertainty in integrated assessment, by type and by component. Probabilistic learning on manifolds (PLoM) is a machine learning technique that constructs a joint probability model of all relevant variables, which may be concentrated on a low-dimensional geometric structure. Compared to traditional density estimation methods, PLoM is more efficient especially when the data are generated by a few latent variables. With the manifold-constrained joint probability model learned by PLoM from a small, initial sample, manifold sampling creates new samples for evaluating converged statistics, which helps answer policy-making questions from prediction, to response, and prevention. As a concrete example, this article reviews IAMs of offshore oil spills—which integrate environmental models, transport models, spill scenarios, and exposure metrics—and demonstrates the use of manifold sampling in assessing the risk of drilling in the Gulf of Mexico.

scholarly journals Stable Matching with Uncertain Linear Preferences

Algorithmica ◽  
2019 ◽  
Vol 82 (5) ◽  
pp. 1410-1433 ◽  
Author(s):  
Haris Aziz ◽  
Péter Biró ◽  
Serge Gaspers ◽  
Ronald de Haan ◽  
Nicholas Mattei ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Probability Model ◽  
Joint Probability ◽  
Stable Matching ◽  
Preference Order ◽  
Limited Information ◽  
Weak Preference ◽  
Restricted Problems ◽  
The Stability ◽  
Joint Probability Model

AbstractWe consider the two-sided stable matching setting in which there may be uncertainty about the agents’ preferences due to limited information or communication. We consider three models of uncertainty: (1) lottery model—for each agent, there is a probability distribution over linear preferences, (2) compact indifference model—for each agent, a weak preference order is specified and each linear order compatible with the weak order is equally likely and (3) joint probability model—there is a lottery over preference profiles. For each of the models, we study the computational complexity of computing the stability probability of a given matching as well as finding a matching with the highest probability of being stable. We also examine more restricted problems such as deciding whether a certainly stable matching exists. We find a rich complexity landscape for these problems, indicating that the form uncertainty takes is significant.

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