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在“自然资源管理”中，命中：13条，耗时：小于0.01 秒

1.A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

作者：Ying Zhaoa ; b ; zhaoying@hit.edu.cn" class="auth_mail ; Ashish Sharmab ; Bellie Sivakumarb ; c ; Lucy Marshallb ; d ; Peng Wanga ; Jiping Jianga

关键词：Water quality modeling ; Seasonal water quality management ; Multi-pollution resources ; Bayesian ; Markov chain Monte Carlo (MCMC)

刊名：Environmental Modelling and Software

年：41821

2.Exploiting Financial News and Social Media Opinions for Stock Market Analysis using MCMC Bayesian Inference

作者：Manolis Maragoudakis ; Dimitrios Serpanos

关键词：Stock return forecasting ; Data mining ; Hierarchical Bayesian methods ; Trading strategies

刊名：Computational Economics

出版者：Springer Netherlands

年：2016

3.Bayesian rainfall frequency analysis with extreme value using the informative prior distribution

作者：Eun-Sung Chung (1) Sang Ug Kim (2)

关键词：rainfall frequency analysis ; extreme value ; Bayesian MCMC ; informative prior distribution

刊名：KSCE Journal of Civil Engineering

年：2013

4.Using informative priors in facies inversion: The case of C-ISR method

作者：G. Valakas ; K. Modis ; kmodis@mail.ntua.gr" class="auth_mail" title="E-mail the corresponding author

关键词：Bayesian facies inversion ; Prior information ; McMC ; Cosimulation

刊名：Advances in Water Resources

年：2016

5.Bayesian approach to tomographic imaging of rock-mass velocity heterogeneities

作者：Wojciech D?bski (1)

关键词：seismic tomography ; Bayesian inversion ; MCMC ; induced seismicity

刊名：Acta Geophysica

年：2013

6.Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes

作者：Xuesong Zhang (1) Xuesong.Zhang@pnl.gov Kaiguang Zhao (2)

关键词：Bayesian neural networks – ; Bayesian model averaging – ; Evolutionary Monte Carlo – ; Hydrologic modeling – ; Streamflow – ; Uncertainty

刊名：Water Resources Management

出版者：Springer Netherlands

年：2012

7.Influence of prior distributions and random effects on count regression models: implications for estimating standing dead tree abundance

作者：Matthew B. Russell (1) 1. Department of Forest Resources ; University of Minnesota ; St. Paul ; MN聽 ; 55108 ; USA

关键词：Biomass ; Carbon ; Coarse woody debris ; Forest inventory ; Markov chain Monte Carlo (MCMC) ; Zero ; inflation

刊名：Environmental and Ecological Statistics

出版者：Springer Netherlands

年：2015

8.A sparse grid based Bayesian method for contaminant source identification

作者：Lingzao Zenga ; Liangsheng Shib ; liangshs@gmail.com ; Dongxiao Zhangc ; Laosheng Wua

关键词：Sparse grid interpolation ; Stochastic collocation method ; MCMC ; Source identification ; Inverse problem

刊名：Advances in Water Resources

年：2012

9.Climate change, wheat productivity and water use in the North China Plain: A new super-ensemble-based probabilistic projection

作者：Fulu Taoa ; taofl@igsnrr.ac.cn ; taofl2002@yahoo.com ; Zhao Zhangb

关键词：Agriculture ; Evaportranspiration ; Food security ; Impact and adaptation ; Water use efficiency ; Yield

刊名：Agricultural and Forest Meteorology

年：2013

10.Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

作者：Raghavendra B. Jana ; a ; raghujana@tamu.edu ; Binayak P. Mohantya

关键词：Bayesian Neural Networks ; Multiscale methods ; Pedotransfer functions ; Remote sensing ; Data ; Non-linear bias correction

刊名：Journal of Hydrology

年：2011

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