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KININMONTHS on King Island

  
 
 
 
 

 
 

Stuart Kininmonth

see also Network Theory in Ecology

Bayesian Belief Networks in Ecology


Tutorial outline
Overview


The purpose of this tutorial is to provide participants with a theoretical and working knowledge of Bayesian Belief Networks applied to ecological systems. The Tutorial is based on a mixture of theory lectures, individual exercises and open discussion sessions. The preferred software is the free Netica program by Norsys.


Goals


The tutorial aims to introduce the basics of Bayesian networks' learning and inference using real-world data to explore the issues commonly found in graphical modelling.


Outline


The tutorial will cover the following topics, with particular attention to Netica practices.
1. Basic concepts and uses of Bayesian networks. Workflow of model estimation and inference: structure learning, parameter learning, exact and approximate inference. Bayesian network interpretations.
2. Structure learning based on expert opinion and model metrics. Discussion of the issues regarding model design (Marcot et al. 2006).
3. Parameter learning: Bayesian and maximum likelihood estimators. Calculation of the conditional probability tables and dealing with missing data.
4. Model evaluation and estimation of prediction accuracy. Confusion matrix and model accuracy metrics.
5. Inference and prediction with the fully developed BBN. Use of cases to predict and explore scenarios.
6. Spatial modelling with BBN and the conceptual development of models in time and space (Stelzenmüller et al. 2010; Kininmonth et al. 2014).


Prerequisites


Background knowledge required for this tutorial includes basic probability theory, ecological knowledge particularly in the area of field-based data collection. Netica is a Windows based software and some competence in the handling of data tables in Excel is required. Spatial modelling requires alternative software such as ArcGIS or R. Apple Mac uses will require a Windows VM such as Parallels.


Intended Audience


Target audience includes researchers and analysts working on data that can be intuitively modelled as networks. Practitioners working in life sciences can relate best with the ecological examples, but the techniques covered in the tutorial can easily be applied to other fields such as social sciences (Koelle et al.).


Workshop Materials


Slides and other materials can be downloaded here soon....


Related Links


https://www.norsys.com/download.html


References


Kininmonth, S., S. Lemm, C. Malone, and T. Hatley. 2014. Spatial vulnerability assessment of anchor damage within the Great Barrier Reef world heritage area, Australia. Ocean and Coastal Management 100: 20–31. pdf


Koelle, D., J. Pfautz, M. Farry, Z. Cox, G. Catto, and J. Campolongo. Applications of Bayesian Belief Networks in Social Network Analysis.


Marcot, B., 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 Journal of Forest Research 36: 3063–3074. doi:10.1139/X06-135.


Stelzenmüller, V., J. Lee, E. Garnacho, and S. I. Rogers. 2010. Assessment of a Bayesian Belief Network-GIS framework as a practical tool to support marine planning. Marine pollution bulletin 60: 1743–54. doi:10.1016/j.marpolbul.2010.06.024.