A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction, disjunction, inverse, and conditional fallacies, as well as unpacking effects and partitioning effects. Quantum probability theory is a general and coherent theory based on a set of (von Neumann) axioms which relax some of the constraints underlying classic (Kolmogorov) pro...

Yalcin [23] shows that Kratzer’s model [5] does not validate some intuitively valid inferences and validates some intuitively invalid ones. He adopts a model based directly on a probability measure. However, as Kratzer [6] says, ‘Our semantic knowledge alone does not give us the precise quantitative notions of probability and desirability that mathematicians and scientists work with’, Yalcin’s ...

In ‘inverse problems’ data from indirect measurements are used to estimate unknown parameters of physical systems. Uncertain data, (possibly vague) prior information on model parameters, and a physical theory relating the model parameters to the observations are the fundamental elements of any inverse problem. Using concepts from probability theory, a consistent formulation of inverse problems ...