Resource
Jump to resource

Object-Oriented Bayesian Networks

Resource description:

An Object-Oriented Bayesian Network (OOBN) is a probabilistic model the uses an intuitive graphical representation to specify dependence and independence relations in a problem domain. It has nodes that represent variables which can represent observed/unobserved quantities, effects, causes of effects, symptoms of effects et cetera. The nodes are joined using directed links that represent direct influences. The influences are quantified using conditional probability distributions. The OOBN also has nodes that represent instances of other network fragments producing a hierarchical model called an OOBN. An OOBN is an encoding of a joint probability distribution supporting the calculation of posterior probability distributions given evidence.

Author/Contact:

Anders L Madsen

Chief Executive Officer

HUGIN EXPERT A/S

Gasværksvej 5

9000 Aalborg

Denmark

Email: anders@hugin.com 

Oppla user name: anderslmadsen 

Publication date:

Images

Requirements:

  • Specification of dependence and independence relations between the variables of the model
  • Quantification of the dependence relations using probabilities
  • Specification of costs, rewards, decision alternatives and information precedence when used to explicitly represent decisions

Advantages:

  • Probabilistic model with a sound handling of uncertainty
  • Knowledge integration model combining data and expert knowledge
  • Intuitive graphical models that handles missing information

Constraints:

  • GIS integration is limited, but improving
  • Dynamic systems are represented using time-sliced models

DOI reference:

10.1007/978-1-4614-5104-4

Additional information:

Barton, D. N., et al. (2012). "Bayesian Networks in Environmental and Resource Management." Integrated Environmental Assessment and Management 8(3): 418–429.

Barton, D. N., et al. (2008). "Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin." Ecological Economics 66: 91-104.

Haines-Young, R. (2011). "Exploring ecosystem service issues across diverse knowledge domains using Bayesian Belief Networks." Progress in Physical Geography 35(5) 681–699.

Licence:

  • Commercial

Development stage:

  • Full, working product
Resource link:
Resource download: