To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen s...To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.展开更多
Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating a...Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.展开更多
Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the ...Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two regions.The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon.The previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically credible.This paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.展开更多
The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simu...The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.展开更多
Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^（st） century for the Tianshan ...Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^（st） century for the Tianshan and northern Kunlun Mountains（TKM） based on the general circulation model（GCM） simulation ensemble from the coupled model intercomparison project phase 5（CMIP5） under the representative concentration pathway（RCP） lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging（BMA） technique. Results show that（1） BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;（2） at the end of the 21^（st） century（2070–2099） under RCP8.5, compared to the control period（1976–2005）, annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;（3） precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and（4） snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease.展开更多
The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model ph...The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.展开更多
It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selectio...It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selection estimator (PMSE) whose properties are hard to derive. Conditioning on data at hand (as it is usually the case), Bayesian model selection is free of this phenomenon. This paper is concerned with the properties of Bayesian estimator obtained after model selection when the frequentist (long run) performances of the resulted Bayesian estimator are of interest. The proposed method, using Bayesian decision theory, is based on the well known Bayesian model averaging (BMA)’s machinery;and outperforms PMSE and BMA. It is shown that if the unconditional model selection probability is equal to model prior, then the proposed approach reduces BMA. The method is illustrated using Bernoulli trials.展开更多
Electrical Source Imaging (ESI) is a non-invasive technique of reconstructing brain activities using EEG data. This technique has been applied to evaluate epilepsy patients being evaluated for epilepsy surgery, showin...Electrical Source Imaging (ESI) is a non-invasive technique of reconstructing brain activities using EEG data. This technique has been applied to evaluate epilepsy patients being evaluated for epilepsy surgery, showing encouraging results for mapping interictal epileptiform discharges (IED). However, ESI is underused in planning epilepsy surgery. This is basically due to the wide availability of methods for solving the electromagnetism inverse problem (e-IP) associated to few studies using EEG setups similar to those most commonly used in clinical setting. In this study, we applied six different methods of solving the e-IP based on IEDs of 20 focal epilepsy patients that presented abnormalities in their MRI. We compared the ESI maps obtained by each method with the location of the abnormality, calculating the Euclidian distances from the center of the lesion to the closest border of the method solution (CL-BM) and also to the solution’s maxima (CL-MM). We also applied a score system in order to allow us to evaluate the sensitivity of each method for temporal and extra temporal patients. In our patients, the Bayesian Model Averaging method had a sensitivity of 86% and the shortest CL-MM. This method also had more restricted solutions that were more representative of epileptogenic activities than those obtained by the other methods.展开更多
Bayesian model averaging (BMA) is a popular and powerful statistical method of taking account of uncertainty about model form or assumption. Usually the long run (frequentist) performances of the resulted estimator ar...Bayesian model averaging (BMA) is a popular and powerful statistical method of taking account of uncertainty about model form or assumption. Usually the long run (frequentist) performances of the resulted estimator are hard to derive. This paper proposes a mixture of priors and sampling distributions as a basic of a Bayes estimator. The frequentist properties of the new Bayes estimator are automatically derived from Bayesian decision theory. It is shown that if all competing models have the same parametric form, the new Bayes estimator reduces to BMA estimator. The method is applied to the daily exchange rate Euro to US Dollar.展开更多
目的:鼻咽癌发病位置隐匿导致早期诊断率低,且具有明显的地域聚集性,是中国一个重要的公共卫生问题。本研究旨在通过2021年全球疾病负担(the Global Burden of Diseases,GBD)数据库分析中国鼻咽癌的疾病负担,为鼻咽癌的精准防控提供流...目的:鼻咽癌发病位置隐匿导致早期诊断率低,且具有明显的地域聚集性,是中国一个重要的公共卫生问题。本研究旨在通过2021年全球疾病负担(the Global Burden of Diseases,GBD)数据库分析中国鼻咽癌的疾病负担,为鼻咽癌的精准防控提供流行病学依据。方法:选取年龄标化发病率、病死率、伤残调整寿命年(disability adjusted life year,DALY)率作为疾病负担的评价指标,按照不同年龄、性别、社会人口学指数及其相关危险因素进行分层分析,同时应用差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型和贝叶斯年龄-时期-队列分析模型(Bayesian age-period-cohort,BAPC)将年龄标化发病率预测至2050年。结果:2021年中国鼻咽癌年龄标化发病率、病死率、DALY率分别为3.4/10万、1.5/10万、48.7/10万,均高于同期全球水平。在所有年龄段,中国男性年龄标化发病率、病死率、DALY率均高于女性。中国鼻咽癌的疾病负担从1990至2021年随着社会人口学指数(socio-demographic index,SDI)的增高逐渐降低。中国归因于饮酒、吸烟、职业甲醛暴露的鼻咽癌疾病负担占比均高于全球水平,且在男性中尤为显著。模型预测中国及全球男性、女性、全人群的年龄标化发病率均提示从2022至2050年呈上升趋势。结论:既往30年中国鼻咽癌的疾病负担随着SDI的升高逐渐降低,但仍高于同期全球水平。同时,中国鼻咽癌的年龄标化发病率在未来30年呈上升趋势。中国仍需进一步增加医疗资源的投入以应对鼻咽癌的防控与诊疗,尤其针对高风险男性群体。展开更多
This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members.Bayesian model averaging(BMA)and regularized linear regression(RLR)are employed...This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members.Bayesian model averaging(BMA)and regularized linear regression(RLR)are employed to address uncertainties arising from different estimation models and variable selection.Jointness is utilized to determine the nature of relationships among variable pairs.The case study spans macroeconomic variables and stock prices from 1996 to 2018.BMA findings reveal a strong positive association between stock price indices and both consumer price index(CPI)and broad money growth in each analyzed OPEC country.Additionally,the study suggests a weak negative correlation between OPEC oil prices and the stock price index.RLR results align with BMA analysis,offering insights valuable for policymakers and international wealth managers.展开更多
基金funding from the Paul ScherrerInstitute,Switzerland through the NES/GFA-ABE Cross Project。
文摘To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.
基金supported by The Technology Innovation Team(Tianshan Innovation Team),Innovative Team for Efficient Utilization of Water Resources in Arid Regions(2022TSYCTD0001)the National Natural Science Foundation of China(42171269)the Xinjiang Academician Workstation Cooperative Research Project(2020.B-001).
文摘Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.
基金supported by the National Key Research and Development Program of China (Grant Nos.2016YFA0602501 and 2018YFA0606004)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant Nos.XDA20040301 and XDA20020201)。
文摘Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two regions.The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon.The previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically credible.This paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.
基金Project supported by the China Special Fund for Meteorological Research in the Public Interest(No.GYHY201306045)the National Natural Science Foundation of China(Nos.41305066 and41575096)
文摘The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.
基金supported by the Thousand Youth Talents Plan(Xinjiang Project)the National Natural Science Foundation of China(41630859)the West Light Foundation of Chinese Academy of Sciences(2016QNXZB12)
文摘Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^（st） century for the Tianshan and northern Kunlun Mountains（TKM） based on the general circulation model（GCM） simulation ensemble from the coupled model intercomparison project phase 5（CMIP5） under the representative concentration pathway（RCP） lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging（BMA） technique. Results show that（1） BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;（2） at the end of the 21^（st） century（2070–2099） under RCP8.5, compared to the control period（1976–2005）, annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;（3） precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and（4） snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease.
基金supported by the National Natural Science Foundation of China(Grant Nos.41405083 and 91437220)the Natural Science Foundation of Hunan Province,China(Grant No.2015JJ3098)+1 种基金the Key Research Program of Frontier Sciences,CAS(QYZDY-SSW-DQC012)the Fund Project for The Education Department of Hunan Province(Grant No.16A234)
文摘The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.
文摘It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selection estimator (PMSE) whose properties are hard to derive. Conditioning on data at hand (as it is usually the case), Bayesian model selection is free of this phenomenon. This paper is concerned with the properties of Bayesian estimator obtained after model selection when the frequentist (long run) performances of the resulted Bayesian estimator are of interest. The proposed method, using Bayesian decision theory, is based on the well known Bayesian model averaging (BMA)’s machinery;and outperforms PMSE and BMA. It is shown that if the unconditional model selection probability is equal to model prior, then the proposed approach reduces BMA. The method is illustrated using Bernoulli trials.
文摘Electrical Source Imaging (ESI) is a non-invasive technique of reconstructing brain activities using EEG data. This technique has been applied to evaluate epilepsy patients being evaluated for epilepsy surgery, showing encouraging results for mapping interictal epileptiform discharges (IED). However, ESI is underused in planning epilepsy surgery. This is basically due to the wide availability of methods for solving the electromagnetism inverse problem (e-IP) associated to few studies using EEG setups similar to those most commonly used in clinical setting. In this study, we applied six different methods of solving the e-IP based on IEDs of 20 focal epilepsy patients that presented abnormalities in their MRI. We compared the ESI maps obtained by each method with the location of the abnormality, calculating the Euclidian distances from the center of the lesion to the closest border of the method solution (CL-BM) and also to the solution’s maxima (CL-MM). We also applied a score system in order to allow us to evaluate the sensitivity of each method for temporal and extra temporal patients. In our patients, the Bayesian Model Averaging method had a sensitivity of 86% and the shortest CL-MM. This method also had more restricted solutions that were more representative of epileptogenic activities than those obtained by the other methods.
文摘Bayesian model averaging (BMA) is a popular and powerful statistical method of taking account of uncertainty about model form or assumption. Usually the long run (frequentist) performances of the resulted estimator are hard to derive. This paper proposes a mixture of priors and sampling distributions as a basic of a Bayes estimator. The frequentist properties of the new Bayes estimator are automatically derived from Bayesian decision theory. It is shown that if all competing models have the same parametric form, the new Bayes estimator reduces to BMA estimator. The method is applied to the daily exchange rate Euro to US Dollar.
文摘目的:鼻咽癌发病位置隐匿导致早期诊断率低,且具有明显的地域聚集性,是中国一个重要的公共卫生问题。本研究旨在通过2021年全球疾病负担(the Global Burden of Diseases,GBD)数据库分析中国鼻咽癌的疾病负担,为鼻咽癌的精准防控提供流行病学依据。方法:选取年龄标化发病率、病死率、伤残调整寿命年(disability adjusted life year,DALY)率作为疾病负担的评价指标,按照不同年龄、性别、社会人口学指数及其相关危险因素进行分层分析,同时应用差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型和贝叶斯年龄-时期-队列分析模型(Bayesian age-period-cohort,BAPC)将年龄标化发病率预测至2050年。结果:2021年中国鼻咽癌年龄标化发病率、病死率、DALY率分别为3.4/10万、1.5/10万、48.7/10万,均高于同期全球水平。在所有年龄段,中国男性年龄标化发病率、病死率、DALY率均高于女性。中国鼻咽癌的疾病负担从1990至2021年随着社会人口学指数(socio-demographic index,SDI)的增高逐渐降低。中国归因于饮酒、吸烟、职业甲醛暴露的鼻咽癌疾病负担占比均高于全球水平,且在男性中尤为显著。模型预测中国及全球男性、女性、全人群的年龄标化发病率均提示从2022至2050年呈上升趋势。结论:既往30年中国鼻咽癌的疾病负担随着SDI的升高逐渐降低,但仍高于同期全球水平。同时,中国鼻咽癌的年龄标化发病率在未来30年呈上升趋势。中国仍需进一步增加医疗资源的投入以应对鼻咽癌的防控与诊疗,尤其针对高风险男性群体。
文摘This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members.Bayesian model averaging(BMA)and regularized linear regression(RLR)are employed to address uncertainties arising from different estimation models and variable selection.Jointness is utilized to determine the nature of relationships among variable pairs.The case study spans macroeconomic variables and stock prices from 1996 to 2018.BMA findings reveal a strong positive association between stock price indices and both consumer price index(CPI)and broad money growth in each analyzed OPEC country.Additionally,the study suggests a weak negative correlation between OPEC oil prices and the stock price index.RLR results align with BMA analysis,offering insights valuable for policymakers and international wealth managers.
文摘蒸散发(Evapotranspiration,ET)是作物需水量的核心组分,也是区域水资源优化配置的关键依据。本文以陕西关中宝鸡峡灌区夏玉米为研究对象,采用BP神经网络(Back propagation neural network,BPNN)、支持向量机(Support vector machine,SVM)、极限学习机(Extreme learning machine,ELM)和极致梯度提升树(eXtreme gradient boosting,XGBoost)4种机器学习算法构建无人机-卫星多源遥感数据协同校正模型,并以最优算法建立的模型校正卫星多光谱数据,实现无人机和卫星数据的尺度转换。利用校正后高精度卫星数据反演夏玉米叶面积指数(Leaf area index,LAI)与株高(Crop height,hc)为蒸散发模型提供数据输入。分别采用双作物系数法、METRIC模型及Penman-Monteith(P-M)冠层阻力模型进行夏玉米蒸散发估算,引入贝叶斯模型平均(Bayesian model averaging,BMA)实现不同生育阶段各方法/模型权重的动态分配,最终得到玉米拔节-完熟期性能稳健的蒸散发BMA融合模型。结果表明:XGBoost算法在夏玉米拔节-完熟期的B/G/R/NIR波段建模精度均为最高,四波段建模结果决定系数(Coefficient of determination,R^(2))较算法ELM高出8.43%、8.67%、6.79%和10.41%;校正后的卫星多光谱数据LAI与hc反演结果R^(2)较原始卫星数据分别平均提高97%和67.5%;BMA融合模型在夏玉米拔节-抽雄期和蜡熟-完熟期较单一最优方法/模型(METRIC模型)均方根误差(Root mean squared error,RMSE)降低39.3%~58.5%。本研究利用“协同校正-动态融合”显著提升了蒸散发遥感监测精度,可为水资源精细化管理提供理论支撑。
