Imprecise probability methods are often claimed to be robust, or more robust than conventional methods. In particular, the higher robustness of the resulting methods seems to be the principal argument support-ing the imprecise probability approach to statistics over the Bayesian one. The goal of the present paper is to investigate the robustness of imprecise probabil-ity methods, and in particular to clarify the termi-nology used to describe this fundamental issue of the imprecise probability approach
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The Bayesian framework for statistical inference offers the possibility of taking expert opinions in...
This thesis provides an exploration of the interplay between imprecise probability and statistics. M...
Often, we only have partial knowledge about a probability distribution, and we would like to select ...
The theory of imprecise probabilities is discussed, and some relations are derived for lower and upp...
This paper deals with algorithms for efficient calculations with imprecise probabilities. All of the...
In recent years, the theory has become widely accepted and has been further developed, but a detaile...
The purpose of this paper is to show that if one adopts conditional probabilities as the primitive c...
The field of of imprecise probability has matured, in no small part because of Teddy Seidenfeld’s de...
The field of of imprecise probability has matured, in no small part because of Teddy Seidenfeld’s de...
This paper investigates the possibility of a frequentist interpretation of imprecise probabilities, ...
The main aim of the paper is to define what the imprecise reliability is, what problems can be solve...
The Bayesian framework for statistical inference offers the possibility of taking expert opinions in...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The Bayesian framework for statistical inference offers the possibility of taking expert opinions in...
This thesis provides an exploration of the interplay between imprecise probability and statistics. M...
Often, we only have partial knowledge about a probability distribution, and we would like to select ...
The theory of imprecise probabilities is discussed, and some relations are derived for lower and upp...
This paper deals with algorithms for efficient calculations with imprecise probabilities. All of the...
In recent years, the theory has become widely accepted and has been further developed, but a detaile...
The purpose of this paper is to show that if one adopts conditional probabilities as the primitive c...
The field of of imprecise probability has matured, in no small part because of Teddy Seidenfeld’s de...
The field of of imprecise probability has matured, in no small part because of Teddy Seidenfeld’s de...
This paper investigates the possibility of a frequentist interpretation of imprecise probabilities, ...
The main aim of the paper is to define what the imprecise reliability is, what problems can be solve...
The Bayesian framework for statistical inference offers the possibility of taking expert opinions in...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...
The paper studies the continuity of rules for updating imprecise probability models when new data ar...