scholarly journals Towards Improving the Efficiency of Bayesian Model Averaging Analysis for Flow in Porous Media via the Probabilistic Collocation Method

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 412 ◽  
Author(s):  
Liang Xue ◽  
Cheng Dai ◽  
Yujuan Wu ◽  
Lei Wang
Keyword(s):  
Porous Media ◽  
Collocation Method ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Flow In Porous Media ◽  
Probabilistic Collocation Method ◽  
Averaging Analysis ◽  
Probabilistic Collocation

scholarly journals Spatial Prediction of N2O Emissions in Pasture: A Bayesian Model Averaging Analysis

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e65039 ◽  
Author(s):  
Xiaodong Huang ◽  
Peter Grace ◽  
Wenbiao Hu ◽  
David Rowlings ◽  
Kerrie Mengersen
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Spatial Prediction ◽  
N2o Emissions ◽  
Averaging Analysis

scholarly journals Which institutions are important for firms performance? Evidence from Bayesian model averaging analysis

2017 ◽  
Vol 64 (4) ◽  
pp. 383-400 ◽  
Author(s):  
Jarko Fidrmuc ◽  
Svatopluk Kapounek ◽  
Martin Siddiqui
Keyword(s):  
Political Institutions ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Financial Conditions ◽  
Long Run ◽  
Averaging Analysis ◽  
Business Regulations ◽  
Excessive Risk Taking ◽  
High Share

Using a rich dataset on individual firms in selected EU countries between 2005 and 2012, we document a surprisingly high share of assets tied in highly inefficient firms. Moreover, we discuss different channels through which institutions may affect firm financial developments and thus the long-run growth. Using Bayesian model averaging analysis, we discuss the importance of different types of economic, financial and political institutions. We show that high institutional quality improves the financial conditions of firms. However, too lax business regulations may worsen firms? performance possibly due to excessive risk taking behavior.

Conditional Simulation of Flow in Heterogeneous Porous Media with the Probabilistic Collocation Method

2014 ◽  
Vol 16 (4) ◽  
pp. 1010-1030 ◽  
Author(s):  
Heng Li
Keyword(s):  
Porous Media ◽  
Hydraulic Conductivity ◽  
Collocation Method ◽  
Heterogeneous Media ◽  
Conditional Simulation ◽  
Stochastic Approach ◽  
Heterogeneous Porous Media ◽  
Propagation Of Uncertainty ◽  
Probabilistic Collocation Method ◽  
Probabilistic Collocation

AbstractA stochastic approach to conditional simulation of flow in randomly heterogeneous media is proposed with the combination of the Karhunen-Loeve expansion and the probabilistic collocation method (PCM). The conditional log hydraulic conductivity field is represented with the Karhunen-Loeve expansion, in terms of some deterministic functions and a set of independent Gaussian random variables. The propagation of uncertainty in the flow simulations is carried out through the PCM, which relies on the efficient polynomial chaos expansion used to represent the flow responses such as the hydraulic head. With the PCM, existing flow simulators can be employed for uncertainty quantification of flow in heterogeneous porous media when direct measurements of hydraulic conductivity are taken into consideration. With illustration of several numerical examples of groundwater flow, this study reveals that the proposed approach is able to accurately quantify uncertainty of the flow responses conditioning on hydraulic conductivity data, while the computational efforts are significantly reduced in comparison to the Monte Carlo simulations.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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