An Alternative Proof That Exact Inference Problem in Bayesian Belief Networks Is NP-Hard

2005 ◽  
pp. 947-955
Author(s):  
Mustafa Tacettin ◽  
Tonguç Ünlüyurt
Keyword(s):  
Bayesian Belief Networks ◽  
Alternative Proof ◽  
Np Hard ◽  
Belief Networks ◽  
Inference Problem ◽  
Exact Inference

Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive

2005 ◽  
Vol 5 (6) ◽  
pp. 95-104 ◽  
Author(s):  
D.N. Barton ◽  
T. Saloranta ◽  
T.H. Bakken ◽  
A. Lyche Solheim ◽  
J. Moe ◽  
...  
Keyword(s):  
Water Quality ◽  
At Risk ◽  
Economic Valuation ◽  
Pollution Abatement ◽  
Network Models ◽  
Cost Effective ◽  
Bayesian Belief Networks ◽  
Probabilistic Interpretation ◽  
Belief Networks ◽  
Benefit Cost Analysis

The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

scholarly journals Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

2021 ◽  
Author(s):  
James D. Karimi ◽  
Jim A. Harris ◽  
Ron Corstanje
Keyword(s):  
Functional Connectivity ◽  
Ecosystem Service ◽  
Urban Areas ◽  
Landscape Connectivity ◽  
Structural Characteristics ◽  
Bird Species ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Patch Area ◽  
Trade Offs

Abstract Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.

Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks

2019 ◽  
Vol 34 (3) ◽  
pp. 2281-2291 ◽  
Author(s):  
Fateme Fahiman ◽  
Steven Disano ◽  
Sarah Monazam Erfani ◽  
Pierluigi Mancarella ◽  
Christopher Leckie
Keyword(s):  
Bayesian Belief Networks ◽  
Data Driven ◽  
Belief Networks
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