A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification

Many classification problems must be performed in a timely or time constrained manner. For this reason, the generation of control schemes which are capable of responding in real-time are fundamental to many applications. For our problem, that of ship classification, tactical scenarios often dictate the response time required from a system. In this paper, we discuss efficient ways to prioritize and gather evidence within belief networks. We also suggest ways in which we can structure our large problem into a series of small ones. This both pre-defines much of our control strategy into the system structure and also localizes our run-time control issues into much smaller networks. The overall control strategy thus includes the combination of both of these methods. By combining them correctly we can reduce the amount of dynamic computation required during run-time and thus improve the responsiveness of the system.

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A real time control strategy for Bayesian belief networks with application to ship classification problem solving

[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence ◽

10.1109/tai.1990.130430 ◽

2002 ◽

Cited By ~ 1

Author(s):

S.A. Musman ◽

L.W. Chang ◽

L.B. Booker

Keyword(s):

Problem Solving ◽

Real Time ◽

Control Strategy ◽

Bayesian Belief Networks ◽

Classification Problem ◽

Belief Networks ◽

Real Time Control ◽

Time Control ◽

Ship Classification

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Path Associative Networks and Bayesian Belief Networks A career decision support tool

PsycEXTRA Dataset ◽

10.1037/e683322011-113 ◽

2000 ◽

Author(s):

Clarence C. Rohrbaugh

Keyword(s):

Decision Support ◽

Decision Support Tool ◽

Career Decision ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Associative Networks ◽

Support Tool

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

Water Science & Technology Water Supply ◽

10.2166/ws.2005.0054 ◽

2005 ◽

Vol 5 (6) ◽

pp. 95-104 ◽

Cited By ~ 4

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.

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

Landscape Ecology ◽

10.1007/s10980-021-01307-6 ◽

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.

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