References
[1]. Cain et al. (1999). Belief networks: a Frame work for
the participatory development of natural resources
management strategies.
[2]. Cheng, J. (1998). “Learning Bayesian Networks from
Data: An Efficient Approach Based on Information
Theory”, Technical Report, Department of Computer
Science, University of Alberta.
[3]. Cooper, G.F., and Herskovits, E. (1992). A Bayesian
Method for the induction of probabilistic networks from
data. Machine Learning, 9 (pp. 309-347).
[4]. Finkel, (1996). An introduction to Bayesian inference
for ecological research and environmental decisionmaking.
Ecol. Appl. 6: 1036-1046.
[5]. Girma Balcha (2002). Conservation and sustainable
use of Forest Genetic Resources. Addis Ababa, Ethiopia.
PP. 150-157. In Proceedings of a National Conference on
Forest Resources of Ethiopia.
[6]. Ham and Kamber (2001). Data mining: concepts and
techniques.
[7]. IUCN (The World Conservation Union), (1994). IUCN
Red List Categories. Gland: 21 PP.
[8]. Heckerman, D. (1995). A tutorial on learning Bayesian
networks. Technical Report MSR-TR-95-06. Microsoft
Research.
[9]. Jeffrey, R. C. (1992). Probability and the art of
judgments. Cambridge University Press,Cambridge, UK.
[10]. Kanga and Kangas (2004). Probability, possibility
and evidence: approaches to consider risk and
uncertainty in forest decisions analysis.
[11]. Kuikka et al. (1999). Modeling environmentally
driven uncertainties in Baltic cod Management by
Bayesian influence diagrams.
[12]. Lauritzen, S. (1988). “Local computations with probabilities on graphical structures and their application to expert systems” in J. Royal Statistics Society B, 50(2),
157-194.
[13]. Marcot, et.al. (2006). Guidelines for Developing and
Updating Bayesian belief network applied to ecological
modeling and conservation, NRC, Canada..
[14]. McNay et al. (2006). A Bayesian Approach to
evaluating habitat for woodland caribou in north-central
British Columbia.
[15]. Mead et al. (2006). Applications of Bayesian
networks in ecological modeling, Montana State
University – Bozeman, USA.
[16]. Namkoong, gene and Koshy, Mathew P. (2000).
Decision Making in gene Conservation. Forset genetic
Resources 28
[17]. Neapolitan, R.E. (2004). “Learning Bayesian
Networks”, Prentice Hall Series in Artificial Intelligence,
[18]. Newton, A., Oldfield, S., Fragoso, G., Mathew, P.,
Miles, L., & Edwards, M. (2003). Towards a Global Tree
Conservation Atlas. UNEP-WCMC/FFI. http://www.unepwcmc.
org/resources/ publications/treeatlas
[19]. Oldfield, S.F., Lusty, C., and MacKinven, A. (1998).
The World List of Threatened Trees. World Conservation
Press, Cambridge
[20]. Olson et al. (1990a). A framework for modeling
uncertain reasoning in ecosystem management II:
Bayesian belief network.
[21]. Pearl, J. (1988). Probabilistic reasoning in intelligent
systems: networks of plausible inference, Morgan
Kaufmann.
[22]. Possingham, H. P. (1997). State-dependent decision
analysis for conservation biology. Pages 298–304 in S. T. A.
Pickett, R. S. Ostfield, M. Shachak, and G. E. Likens,
editors. The ecological basis of conser vation:
heterogeneity, ecosystems and biodiversity., Chapman
and Hall, New York, New York, USA.
[23]. Rahel Bekele (2005). Computer-Assisted Learner
Group Formation Based on Personality Traits, Hamburg,
Germany.
[24]. Regan, H.M., M. Colyvan, and M.A. Burgman.
(2002). A taxonomy and treatment of uncertainty for
ecology and conser vation biology. Ecological
Applications, 12:618–628.
[25]. Samir Abduselam (2001). An Application of Expert
Systems on Species Selection: The case of Forestry
Research Center.
[26]. Sando, T. (2005). Modeling Highway Crashes Using
Bayesian Networks: The Florida State University, College of
Engineering.
[27]. Schroth, G., et.al. (1996). Forest Ecology and
Management, Volume 84, Issues 1-3, pp. 199- 208.
[28]. Spirtes, P. (2000). “Causation, Prediction and
Search”, 2nd Edition, MIT Press,
[29]. Taye Bekele, Kumelachew Yeshitila, Shiferaw Dessie
and Günther Haase (2002). Priority Woody Species of the
Moist Montane Forests of Southwest Ethiopia:
Consideration for Conservation.
[30]. Whitten, I.H., (2005). Data mining: Practical
Machine Learning tools and techniques, Second Edition.