Panchalingam, Geethanjali. Ph.D., Purdue University, May 1992. Uncertainty Modeling of Many Correlated and Skewed Random Variables. Major Professor: Milton E. Harr. This thesis presents a point estimate methodology directly suited to uncertainty modeling for systems with many correlated and skewed random variables. The methodology proposes two models. The models provide a simple but powerful pr...

In this communication, we study decision influence diagrams using fuzzy sets and fuzzy random variables t,o model problems in which assessing real-valued ~t i l i t~ies , and employing real-valued random variables t o model random magnit,udes, are considered to be too restrictive. We propose conditions allowing us to solve decision influence diagrams when uncertainty magnitudes are formalized b...

abstarct development of greenhouse gases in future periods not only causes change in average amounts of climate variables but also makes variables of this variability affected. then for sure concerning alternations followed with climate variables’ fluctuations and its average amounts in effecting on runoff would make more reliable results. in this inquiry initially fluctuations and average amou...

Knowledge about complex events is usually incomplete in practice. Zeros can be utilized to capture such events within probability models. In this article, Geiger and Pearl’s conditional probabilistic independence statements are investigated in the presence of zeros. Random variables can be specified to be zero-free, i.e., to disallow zeros in their domains. Zero-free random variables provide an...

This paper illustrates how interval analysis can be used as a basis for generalized models of uncertainty. When epistemic uncertainty is presented as a range and the aleatory is based on available information, or when random variables are assigned an interval probability, the uncertainty will have a Probability Bound (PB) structure. When Interval Monte Carlo (IMC) is used to sample random varia...

Traditional models of decision-making under uncertainty assume perfect information, i.e., accurate values for the system parameters and specific probability distributions for the random variables. However, such precise knowledge is rarely available in practice, and a strategy based on erroneous inputs might be infeasible or exhibit poor performance when implemented. The purpose of this tutorial...

A risk-minimizing portfolio model under uncertainty with randomness and fuzziness is discussed. By a perception-based extension of estimations for fuzzy random variables, the risk-minimizing portfolio problem is developed. In the uncertainty model, the randomness and fuzziness are evaluated respectively by the probabilistic expectation and mean values with evaluation weights and -mean functions...