This paper explores the collective risk model as a vehicle for estimating the probability distribution for reserves. Though this basic model has been suggested in the past and It provides a direct means to estimate process uncertainty, it does not directly address the potentially more significant problem of parameter uncertainty. This paper presents some techniques to estimate parameter uncerta...

In this reflection, Anastasia Shesterinina introduces her new book, Mobilizing in Uncertainty: Collective Identities and War Abkhazia (Cornell University Press, 2021). We invited Nina Caspersen at the of York, Jesse Driscoll California, San Diego, Edward Schatz Toronto to comment on book. Their commentaries are followed by a response from author.

on account of the existence of uncertainty, dea occasionally faces the situation of imprecise data, especially when a set of dmus include missing data, ordinal data, interval data, stochastic data, or fuzzy data. therefore, how to evaluate the efficiency of a set of dmus in interval environments is a problem worth studying. in this paper, we discussed the new method for evaluation and ranking i...

We consider differentially private inductive logic programming. We begin by formulating the problem of guaranteeing differential privacy to inductive logic programming, and then prove the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that when the siz...

Induction-induction is a principle for defining data types in Martin-Löf Type Theory. An inductive-inductive definition consists of a set A, together with an A-indexed family B : AÑ Set, where both A and B are inductively defined in such a way that the constructors for A can refer to B and vice versa. In addition, the constructors for B can refer to the constructors for A. We extend the usual i...

Although evidence in real life is often uncertain, the psychology of inductive reasoning has, so far, been confined to certain evidence. The present study extends previous research by investigating whether people properly estimate the impact of uncertain evidence on a given hypothesis. Two experiments are reported, in which the uncertainty of evidence is explicitly (by means of numerical values...

Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed at combining the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. An ex...