Bayesian Networks ~ Applications

Guidelines to Bayesian Network Modeling

Selected References With Information on
Creating and Testing Bayesian Network Models

Cain, J. 2001. Planning improvements in natural resources management: guidelines for using Bayesian networks to support the planning and management of development programmes in the water sector and beyond. Centre for Ecology & Hydrology, Crowmarsh Gifford, Wallingford, Oxon, U.K. 124 pp.

Kjaerulff, U. B., and A. L. Madsen. 2007. Bayesian networks and influence diagrams: a guide to construction and analysis. Springer, 318 pp.

Marcot, B. G. 2006. Characterizing species at risk I: modeling rare species under the Northwest Forest Plan. Ecology and Society 11(2):10. [online] http://www.ecologyandsociety.org/vol11/iss2/art10/ ... or article PDF (701KB) and appendix PDF (21KB)

Marcot, B. G., P. A. Hohenlohe, S. Morey, R. Holmes, R. Molina, M. Turley, M. Huff, and J. Laurence. 2006. Characterizing species at risk II: using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan. Ecology and Society 11(2):12. [online] http://www.ecologyandsociety.org/vol11/iss2/art12/ ... or article PDF (1.3MB)
    Abstract:
We developed a set of decision-aiding models as Bayesian belief networks (BBNs) that represented a complex set of evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest, U.S. The models were used in a structured assessment and paneling procedure as part of an adaptive management process that evaluated new scientific information under the Northwest Forest Plan. The models were not prescriptive but helped resource managers and specialists to evaluate complicated and at times conflicting conservation guidelines and to reduce bias and uncertainty in evaluating the scientific data. We concluded that applying the BBN modeling framework to complex and equivocal evaluation guidelines provided a set of clear, intuitive decision-aiding tools that greatly aided the species evaluation process.

Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann. 2006. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36:3063-3074. PDF (492KB)
   Abstract:
Bayesian belief networks (BBNs) are useful tools for modeling ecological predictions and aiding resource-management decision-making. We provide practical guidelines for developing, testing, and revising BBNs. Primary steps in this process include creating influence diagrams of the hypothesized "causal web" of key factors affecting a species or ecological outcome of interest; developing a first, alpha-level BBN model from the influence diagram; revising the model after expert review; testing and calibrating the model with case files to create a beta-level model; and updating the model structure and conditional probabilities with new validation data, creating the final-application gamma-level model. We illustrate and discuss these steps with an empirically based BBN model of factors influencing probability of capture of northern flying squirrels (Glaucomys sabrinus (Shaw)). Testing and updating BBNs, especially with peer review and calibration, are essential to ensure their credibility and reduce bias. Our guidelines provide modelers with insights that allow them to avoid potentially spurious or unreliable models.

McCann, R., B. G. Marcot, and R. Ellis. 2006. Bayesian belief networks: applications in natural resource management. Canadian Journal of Forest Research 36:3053-3062. PDF (241KB)
    Abstract:
In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the influence of uncertainty and variability on costs and benefits assigned to model outcomes or decisions associated with natural resource management. BBN tools also lend themselves well to an adaptive-management framework by posing testable management hypotheses and incorporating new knowledge to evaluate existing management guidelines.

Pourret, O., B. G. Marcot, and P. Naïm, ed. 2008. Bayesian belief networks: a practical guide to applications. Wiley, 432 pp. You are here!
   Abstract:
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks.