Copula-based joint probability function for PGA and CAV: a case study from Taiwan

2016 ◽  
Vol 45 (13) ◽  
pp. 2123-2136 ◽  
Author(s):  
Yun Xu ◽  
Xiao-Song Tang ◽  
J. P. Wang ◽  
H. Kuo-Chen
Keyword(s):  
Probability Function ◽  
Joint Probability ◽  
Joint Probability Function

scholarly journals A stochastic approach to handle resource constraints as knapsack problems in ensemble pruning

2021 ◽  
Author(s):  
András Hajdu ◽  
György Terdik ◽  
Attila Tiba ◽  
Henrietta Tomán
Keyword(s):  
Probability Function ◽  
Resource Constraints ◽  
Joint Probability ◽  
Stochastic Approach ◽  
Stochastic Search ◽  
Search Process ◽  
Common Solution ◽  
The Common ◽  
The Individual ◽  
Joint Probability Function

AbstractEnsemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function, which takes the common solution tools—such as dynamic programming—out of action. We introduce a novel stochastic approach that considers the energy as the joint probability function of the member accuracies. This type of knowledge can be efficiently incorporated in a stochastic search process as a stopping rule, since we have the information on the expected accuracy or, alternatively, the probability of finding more accurate ensembles. Experimental analyses of the created ensembles of pattern classifiers and object detectors confirm the efficiency of our approach over other pruning ones. Moreover, we propose a novel stochastic search method that better fits the energy, which can be incorporated in other stochastic strategies as well.

scholarly journals The nested structure of urban business clusters

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Clémentine Cottineau ◽  
Elsa Arcaute
Keyword(s):  
Percolation Theory ◽  
Probability Function ◽  
Joint Probability ◽  
City Region ◽  
Economic Activities ◽  
National Statistics ◽  
Industrial Sectors ◽  
Nested Structure ◽  
Nested Hierarchy ◽  
Joint Probability Function

AbstractAlthough the cluster theory literature is bountiful in economics and regional science, there is still a lack of understanding of how the geographical scales of analysis (neighbourhood, city, region) relate to one another and impact the observed phenomenon, and to which extent the clusters are industrially coherent or geographically consistent. In this paper, we cluster spatial economic activities through a multi-scalar approach making use of percolation theory. We consider both the industrial similarity and the geographical proximity between firms, through their joint probability function which is constructed as a copula. This gives rise to an emergent nested hierarchy of geoindustrial clusters, which enables us to analyse the relationships between the different scales, and specific industrial sectors. Using longitudinal business microdata from the Office for National Statistics, we look at the evolution of clusters which spans from very local groups of businesses to the metropolitan level, in 2007 and in 2014, so that the changes stemming from the financial crisis can be observed.

scholarly journals Deriving Design Flood Hydrograph Based on Conditional Distribution: A Case Study of Danjiangkou Reservoir in Hanjiang Basin

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Changjiang Xu ◽  
Jiabo Yin ◽  
Shenglian Guo ◽  
Zhangjun Liu ◽  
Xingjun Hong
Keyword(s):  
Joint Probability ◽  
Flood Frequency ◽  
Peak Discharge ◽  
Flood Frequency Analysis ◽  
Design Flood ◽  
Copula Functions ◽  
Danjiangkou Reservoir ◽  
Flood Peak ◽  
Flood Hydrograph

Design flood hydrograph (DFH) for a dam is the flood of suitable probability and magnitude adopted to ensure safety of the dam in accordance with appropriate design standards. Estimated quantiles of peak discharge and flood volumes are necessary for deriving the DFH, which are mutually correlated and need to be described by multivariate analysis methods. The joint probability distributions of peak discharge and flood volumes were established using copula functions. Then the general formulae of conditional most likely composition (CMLC) and conditional expectation composition (CEC) methods that consider the inherent relationship between flood peak and volumes were derived for estimating DFH. The Danjiangkou reservoir in Hanjiang basin was selected as a case study. The design values of flood volumes and 90% confidence intervals with different peak discharges were estimated by the proposed methods. The performance of CMLC and CEC methods was also compared with conventional flood frequency analysis, and the results show that CMLC method performs best for both bivariate and trivariate distributions which has the smallest relative error and root mean square error. The proposed CMLC method has strong statistical basis with unique design flood composition scheme and provides an alternative way for deriving DFH.

scholarly journals Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China

2021 ◽  
Author(s):  
Yujie Li ◽  
Bin Xu ◽  
Dong Wang ◽  
QJ Wang ◽  
Xiongwei Zheng ◽  
...  
Keyword(s):  
General Circulation ◽  
Critical Role ◽  
Joint Probability ◽  
General Circulation Models ◽  
Mean Value ◽  
Monthly Precipitation ◽  
Post Processing ◽  
Probabilistic Evaluation ◽  
Forecasting Performance

Abstract Monthly Precipitation Forecasts (MPF) play a critical role in drought monitoring, hydrological forecasting and water resources management. In this study, we applied two advanced Machine Learning Models (MLM) and latest General Circulation Models (GCM) to generate deterministic MPFs with a resolution of 0.5° across China. Then the Bayesian Joint Probability (BJP) modeling approach is employed to calibrate and generate corresponding ensemble MPFs. Raw and post-processing MPFs were put against gridded observations over the period of 1981–2015. The results indicated that: (1) for deterministic evaluation, the forecasting performance of MLMs was more inclined to generate random forecasts around the mean value, while the GCMs could reflect the increasing or decreasing trend of precipitation to some degree; (2) for probabilistic evaluation, the four BJP calibrated ensemble MPFs were unbiased and reliable. Compared to climatology, reliability and sharpness were all significantly improved. However, in terms of overall accuracy metric, the ensemble MPFs generated from MLMs were similar to climatology. In contrast, the ensemble MPFs generated from GCMs achieved better forecasting skill and was not dependent on forecasting regions and months. Moreover, the post-processing method is necessary that achieve not only bias-free but also reliable as well as skillful ensemble MPFs.

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