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马克思主义、列宁主义、毛泽东思想、邓小平理论(4)
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天文学、地球科学(1)
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.
Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling
作者:
Hongxiang Yan
;
yan2@pdx.edu" class="auth_mail" title="E-mail the corresponding author
;
Hamid Moradkhani hamidm@pdx.edu" class="auth_mail" title="E-mail the corresponding author
关键词:
Data assimilation
;
Particle filter
;
Markov
chain
Monte
Carlo
;
Soil moisture
;
Geostatistical modeling
;
ASCAT
刊名:Advances in Water Resources
年:2016
3.
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
4.
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
5.
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
6.
Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump
Markov
chain
Monte
Carlo
methods
作者:
A. Mondal
;
Y. Efendiev
;
B. Mallick
;
A. Datta-Gupta
关键词:
Reversible jump
;
MCMC
;
Porous media
;
Upscaling
;
Multiscale
;
Facies
;
Level sets
刊名:Advances in Water Resources
年:2010
7.
Bayesian Analysis of the Discovery Process Model Using
Markov
Chain
Monte
Carlo
作者:
Richard Sinding-Larsen and Jingzhen Xu
关键词:
KEY WORDS
;
Markov
chain
Monte
Carlo
(
MCMC
)
;
discovery process model
;
bayesian estimation
;
metropolis–
;
Hastings algorithm
;
play analysis.
刊名:Natural Resources Research
年:2005
8.
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
9.
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
10.
Spatiotemporal dynamics of landscape pattern and hydrologic process in watershed systems
作者:
Timothy O. Randhir
;
a
;
Randhir@eco.umass.edu"" rel=""nofollow
;
trandhir@gmail.com"" rel=""nofollow
;
Olga Tsvetkovaa
关键词:
Nonpoint source pollution
;
Spatio-temporal modeling
;
Watershed management
;
Runoff
;
GIS
;
Dynamic modeling
刊名:Journal of Hydrology
年:2011
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