The concept of cointegration is widely used in applied non-stationary time series analysis to describe the co-movement of data measured over time. In this paper, we proposed a Bayesian model for cointegration test and...The concept of cointegration is widely used in applied non-stationary time series analysis to describe the co-movement of data measured over time. In this paper, we proposed a Bayesian model for cointegration test and analysis, based on the dynamic latent factor framework. Efficient computational algorithms are also developed based on Markov Chain Monte Carlo (MCMC). Performance and efficiency of the the model and approaches are assessed by simulated and real data analysis.展开更多
To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. I...To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed me-thod, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of the adjacent areas are used to estimate the priori probability density func-tions of the endmembers in the current area, which works as a type of constraint in the iterative spectral unmixing process. Experimental results demonstrate the effectivity and efficiency of the proposed method both for synthetic and real hyperspectral images, and it can provide a useful tool for spatial correlation and comparation analysis between ad-jacent or similar areas.展开更多
A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times...A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.展开更多
研究渔业资源与环境因子的关系,并了解种群分布对环境变化的响应机制,是养护渔业资源、实现渔业可持续发展的基础。渔业资源丰度和种群分布受多种环境因素影响,但目前的研究更多关注环境因素的直接影响,较少考虑环境因素间的相互作用。...研究渔业资源与环境因子的关系,并了解种群分布对环境变化的响应机制,是养护渔业资源、实现渔业可持续发展的基础。渔业资源丰度和种群分布受多种环境因素影响,但目前的研究更多关注环境因素的直接影响,较少考虑环境因素间的相互作用。为了探索不同环境因素对马达加斯加西海岸虾类资源量的影响机制与路径,本研究使用2014-2020年该海域捕虾拖网数据,采用贝叶斯网络分析了降水、径流等海洋环境因子与3种主捕虾类单位捕捞努力量渔获量(catch per unite effort,CPUE)之间的网络关系,探索在多种环境因子影响下3种虾类CPUE的潜在驱动因素。研究结果表明:降水、径流、海面高度距平(sea surface height anomaly,SSHA)和海表面温度(seasurface temperat-ure,SST)是影响印度白虾CPUE的主要因素,径流、SSHA、SST和叶绿素a质量浓度(chlorophyll a mass concentration,Chl a)是影响独角新对虾和短沟对虾的CPUE的主要因素;降水通过不同路径影响其他环境因子进而对3种虾类CPUE产生间接影响:降水通过径流、SST和SSHA的途径对印度白虾产生间接影响,但对于独角新对虾和短沟对虾,降水通过径流、SST、SSHA和Chla的途径产生间接影响。研究结果揭示了马达加斯加西海岸降水和其他海洋环境因子不仅对3种虾类CPUE产生直接影响,降水还能通过影响其他环境因子对虾类种群资源变动的间接影响途径和机制。展开更多
文摘The concept of cointegration is widely used in applied non-stationary time series analysis to describe the co-movement of data measured over time. In this paper, we proposed a Bayesian model for cointegration test and analysis, based on the dynamic latent factor framework. Efficient computational algorithms are also developed based on Markov Chain Monte Carlo (MCMC). Performance and efficiency of the the model and approaches are assessed by simulated and real data analysis.
文摘To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed me-thod, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of the adjacent areas are used to estimate the priori probability density func-tions of the endmembers in the current area, which works as a type of constraint in the iterative spectral unmixing process. Experimental results demonstrate the effectivity and efficiency of the proposed method both for synthetic and real hyperspectral images, and it can provide a useful tool for spatial correlation and comparation analysis between ad-jacent or similar areas.
文摘A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.
文摘研究渔业资源与环境因子的关系,并了解种群分布对环境变化的响应机制,是养护渔业资源、实现渔业可持续发展的基础。渔业资源丰度和种群分布受多种环境因素影响,但目前的研究更多关注环境因素的直接影响,较少考虑环境因素间的相互作用。为了探索不同环境因素对马达加斯加西海岸虾类资源量的影响机制与路径,本研究使用2014-2020年该海域捕虾拖网数据,采用贝叶斯网络分析了降水、径流等海洋环境因子与3种主捕虾类单位捕捞努力量渔获量(catch per unite effort,CPUE)之间的网络关系,探索在多种环境因子影响下3种虾类CPUE的潜在驱动因素。研究结果表明:降水、径流、海面高度距平(sea surface height anomaly,SSHA)和海表面温度(seasurface temperat-ure,SST)是影响印度白虾CPUE的主要因素,径流、SSHA、SST和叶绿素a质量浓度(chlorophyll a mass concentration,Chl a)是影响独角新对虾和短沟对虾的CPUE的主要因素;降水通过不同路径影响其他环境因子进而对3种虾类CPUE产生间接影响:降水通过径流、SST和SSHA的途径对印度白虾产生间接影响,但对于独角新对虾和短沟对虾,降水通过径流、SST、SSHA和Chla的途径产生间接影响。研究结果揭示了马达加斯加西海岸降水和其他海洋环境因子不仅对3种虾类CPUE产生直接影响,降水还能通过影响其他环境因子对虾类种群资源变动的间接影响途径和机制。
