Abstract
Two different types of uncertainty have been recognized in different literatures. Swiler et al. define epistemic uncertainty as lack of knowledge about the appropriate value to use for quantity (also known as type B, subjective, reducible) which researcher can reduce the level of uncertainty by increasing introduction of the relevant data, and aleatory uncertainty as the type of uncertainty which is characterized by its inherent randomness (type A, stochastic, irreducible) which cannot be reduced by acquiring additional data.
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References
Swiler, L. P., Paez, T. L., & Mayes, R. L. (2009). Epistemic uncertainty quantification tutorial. Proceedings of the IMAC XXVII Conference and Exposition on Structural Dynamics.
Kiureghian, A. D. (2007). Aleatory or epistemic? Does it matter? Special Workshop on Risk Acceptance and Risk Communication March 26–27, 2007, Stanford University, University of California, Berkeley.
Khazaii, J. (2014). Energy efficient HVAC design, an essential guide for sustainable building. New York, NY: Springer.
American Society of Heating, Refrigerating and Air Conditioning Engineers. (2013). ASHRAE 90.1 Standard: Energy Standard for buildings except low-rise residential buildings. Atlanta, GA: American Society of Heating, Refrigerating and Air Conditioning Engineers.
Hulett & Associates web site; use decision Trees to make important project decision; http://www.projectrisk.com/decision_tree_for_important_project_decisions.html
Schultz, M. T., Mitchell, K. N., & Harper, B. K. (2010). Decision making under uncertainty. ERDC TR-10-12. U.S. Army Corps of Engineers.
Online statistics education: An interactive multimedia course of study; Developed by Rice University (Lead Developer) and University of Houston Clear Lake and Tufts University. http://onlinestatbook.com/2/calculators/normal_dist.html
Hansson, S. O. (2005). Decision theory, a brief introduction. Stockholm: Royal Institute of Technology.
Negnevitsky, M. (2005). Artificial intelligence, a guide to intelligent systems. Harlow: Pearson Education.
Sebastiani, P. (2010) A tutorial on probability theory. Department of Mathematics and Statistics; University of Massachusetts at Amherst. http://www.sci.utah.edu/~gerig/CS6640-F2010/prob-tut.pdf
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Khazaii, J. (2016). Decision Making Under Uncertainty. In: Advanced Decision Making for HVAC Engineers. Springer, Cham. https://doi.org/10.1007/978-3-319-33328-1_12
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DOI: https://doi.org/10.1007/978-3-319-33328-1_12
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