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Combining state and transition models with dynamic Bayesian networks

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  • Nicholson, Ann E.
  • Flores, M. Julia

Abstract

Bashari et al. (2009) propose combining state and transition models (STMs) with Bayesian networks for decision support tools where the focus is on modelling the system dynamics. There is already an extension of Bayesian networks – so-called dynamic Bayesian networks (DBNs) – for explicitly modelling systems that change over time, that has also been applied in ecological modelling. In this paper we propose a combination of STMs and DBNs that overcome some of the limitations of Bashari et al.’s approach including providing an explicit representation of the next state, while retaining its advantages, such an the explicit representation of transitions. We then show that the new model can be applied iteratively to predict into the future consistently with different time frames. We use Bashari et al.’s rangeland management problem as an illustrative case study. We present a comparative complexity analysis of the different approaches, based on the structure inherent in the problem being modelled. This analysis showed that any models that explicitly represent all the transitions only remain tractable when there are natural constraints in the domain. Thus we recommend modellers should analyse these aspects of their problem before deciding whether to use the framework.

Suggested Citation

  • Nicholson, Ann E. & Flores, M. Julia, 2011. "Combining state and transition models with dynamic Bayesian networks," Ecological Modelling, Elsevier, vol. 222(3), pages 555-566.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:3:p:555-566
    DOI: 10.1016/j.ecolmodel.2010.10.010
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    References listed on IDEAS

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    1. Sadler, Rohan J. & Hazelton, Martin & Boer, Matthias M. & Grierson, Pauline F., 2010. "Deriving state-and-transition models from an image series of grassland pattern dynamics," Ecological Modelling, Elsevier, vol. 221(3), pages 433-444.
    2. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    3. Renken, Henk & Mumby, Peter J., 2009. "Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach," Ecological Modelling, Elsevier, vol. 220(9), pages 1305-1314.
    4. Ahmed Rebai (ed.), 2010. "Bayesian Network," Books, IntechOpen, number 786, January-J.
    5. Saatkamp, H. W. & Huirne, R. B. M. & Geers, R. & Dijkhuizen, A. A. & Noordhuizen, J. P. T. M. & Goedseels, V., 1996. "State-Transition modelling of classical swine fever to evaluate national identification and recording systems -- General aspects and model description," Agricultural Systems, Elsevier, vol. 51(2), pages 215-236, June.
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    2. Smith, Ron I. & Barton, David N. & Dick, Jan & Haines-Young, Roy & Madsen, Anders L. & Rusch, Graciela M. & Termansen, Mette & Woods, Helen & Carvalho, Laurence & Giucă, Relu Constantin & Luque, Sandr, 2018. "Operationalising ecosystem service assessment in Bayesian Belief Networks: Experiences within the OpenNESS project," Ecosystem Services, Elsevier, vol. 29(PC), pages 452-464.
    3. Maldonado, A.D. & Aguilera, P.A. & Salmerón, A. & Nicholson, A.E., 2018. "Probabilistic modeling of the relationship between socioeconomy and ecosystem services in cultural landscapes," Ecosystem Services, Elsevier, vol. 33(PB), pages 146-164.
    4. Vilizzi, L. & Price, A. & Beesley, L. & Gawne, B. & King, A.J. & Koehn, J.D. & Meredith, S.N. & Nielsen, D.L. & Sharpe, C.P., 2012. "The belief index: An empirical measure for evaluating outcomes in Bayesian belief network modelling," Ecological Modelling, Elsevier, vol. 228(C), pages 123-129.

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