A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

Abstract The classical credibility theory is a cornerstone of experience rating, especially in the field property and casualty insurance. An obstacle to putting into practice conversion available prior information precise choice crucial hyperparameters. In most real-world applications, necessary justify lacking, so we propose an imprecise estimator that honestly acknowledges imprecision hyperpa...

The objective of this work is to quantify the uncertainty in probability failure estimates resulting from incomplete knowledge distributions for input random variables. We propose a framework that couples widely used Subset simulation (SuS) with Bayesian/information theoretic multi-model inference. process starts data infer model inputs. Often such sets are small. Multi-model inference assess a...

A traditional theory of uncertainty says that beliefs come in degrees. Degrees of belief (“credences”) have real number values between  and , where  conventionally represents certain belief,  represents certain disbelief, and values in between represent degrees of uncertainty. We have elegant, well-understood normative theories for credences: norms for how credences should hang together at ...

The risk response development is one of the main phases in the project risk management that has major impacts on a large-scale project’s success. Since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. Conventional approaches tend to be less effective in dealing with the imprecise of the risk response deve...