There have been a number of previous successful efforts that show how fuzzy logic concepts have homomorphic-like stochastic correspondences, utilizing one-point coverages of appropriately constructed random sets. Independent of this and fuzzy logic considerations in general, boolean relational event algebra (BREA) has been introduced within a stochastic setting for representing prescribed compo...

P(Z) logic offers a new way to reason about vagueness (ie fuzziness), that treats fuzziness as degrees, distinct from probabilities. One then applies probability distributions over fuzziness. This approach is different from both classical fuzzy logic [26] and possibility theory [1]. P(Z) logic is specially designed for common-sense reasoning.

fault tree analysis (fta) as a probabilistic risk assessment (pra) method is used to identify basic causes leading to an undesired event, to represent logical relation of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event. to conduct a quantitative fta, one needs a fault tree along with failure data of the basic events (bes). someti...

Genetic algorithms are commonly used to solve combinatorial optimization problems. The implementation evolves using genetic operators (crossover, mutation, selection, etc.). Anyway, genetic algorithms like some other methods have parameters (population size, probabilities of crossover and mutation) which need to be tune or chosen. In this paper, our project is based on an existing hybrid geneti...

In this paper, we show how our AI opponents learn internal representations of probabilities. We use a Bayesian interpretation of such subjectivist probabilities but do not implement full Bayesian methods of parameter estimation since we wish the AIs to be as human-like as possible. Thus the parameters of the subjectivist probabilities are learned incrementally.

Markov chains (MCs) are widely used to model a great deal of financial and actuarial problems. Likewise, they also in many other fields ranging from economics, management, agricultural sciences, engineering or informatics medicine. This paper focuses on the use MCs for design non-life bonus-malus systems (BMSs). It proposes quantifying uncertainty transition probabilities BMSs by using fuzzy nu...

Fuzzy logic, decision making procedure (AHP), and conditional probability were evaluated on the spatial analysis of geological data, to address potential areas for radioactive mineral occurrences in the Poços de Caldas Plateau ( ≅ 750 Km). Spatial inference techniques were applied controlled by a prospecting model based on diagnostic criteria, represented by favorable lithology, structures feat...

Risk assessment in regions with low earthquake activity is important for reinsurance companies and governmental building authorities. They need a complete picture of the possible risks. Even contradictive opinions have to be taken into account. Data for this kind of analysis, especially in natural disasters, is of poor quality. Standard statistical analysis is not possible. Extreme values are r...

We review two existing interpretations of fuzzy random variables. In the first one, the fuzzy random variable is viewed as a linguistic random variable. In the second case, it represents some incomplete knowledge about an otherwise standard random variable. For each interpretation, the information provided by the frv is described by a specific model, namely a standard probability model and a se...

In this paper, we investigate the probabilistic decision making problems with hesitant fuzzy information, some new probabilistic decision making analysis methods are developed. Then, we have developed some new probability aggregation operators with hesitant fuzzy information: probability hesitant fuzzy Hamacher weighted average(P-HFHWA) operator, immediate probability hesitant fuzzy Hamacher or...