Add to ORKGABSTRACT. The generalized Bayes ’ rule (GBR) can be used to conduct ‘quasi-Bayesian ’ analyses when prior beliefs are represented by imprecise probability models. We describe a procedure for deriving coherent imprecise probability models when the event space consists of a finite set of mutually exclusive and exhaustive events. The procedure is based on Walley’s theory of upper and lower prevision and employs simple linear programming models. We then describe how these models can be updated using Cozman’s linear programming formulation of the GBR. Examples are provided to demonstrate how the GBR can be applied in practice. These examples also illustrate the effects of prior imprecision and prior-data conflict on the precision of the posterio...
Bayesian inference is a method of statistical inference in which all forms of uncertainty are expres...
Bayesian inference and decision making requires elicitation of prior probabilities and sampling dist...
Two major approaches have developed within Bayesian statistics to address uncer-tainty in the prior ...
The generalized Bayes’ rule (GBR) can be used to conduct ‘quasi-Bayesian’ analyses when prior belief...
A great advantage of imprecise probability models over models based on precise, traditional probabil...
This paper discusses fundamental aspects of inference with imprecise probabilities from the decisio...
By its capability to deal with the multidimensional nature of uncertainty, imprecise probability pro...
The Bayesian framework for statistical inference offers the possibility of taking expert opinions in...
Two major approaches have developed within Bayesian statistics to address uncertainty in the prior d...
We present Bayesian updating of an imprecise probability measure, represented by a class of precise ...
We solve two fundamental problems of probabilistic reasoning: given finitely many conditional probab...
Bayesian inference enables combination of observations with prior knowledge in the reasoning process...
AbstractThis article investigates the computation of posterior upper expectations induced by impreci...
In the Bayesian approach to statistical inference, possibly subjective knowledge on model parameters...
AbstractA new approach to constructing generalised probabilities is proposed. It is based on the mod...
Bayesian inference is a method of statistical inference in which all forms of uncertainty are expres...
Bayesian inference and decision making requires elicitation of prior probabilities and sampling dist...
Two major approaches have developed within Bayesian statistics to address uncer-tainty in the prior ...
The generalized Bayes’ rule (GBR) can be used to conduct ‘quasi-Bayesian’ analyses when prior belief...
A great advantage of imprecise probability models over models based on precise, traditional probabil...
This paper discusses fundamental aspects of inference with imprecise probabilities from the decisio...
By its capability to deal with the multidimensional nature of uncertainty, imprecise probability pro...
The Bayesian framework for statistical inference offers the possibility of taking expert opinions in...
Two major approaches have developed within Bayesian statistics to address uncertainty in the prior d...
We present Bayesian updating of an imprecise probability measure, represented by a class of precise ...
We solve two fundamental problems of probabilistic reasoning: given finitely many conditional probab...
Bayesian inference enables combination of observations with prior knowledge in the reasoning process...
AbstractThis article investigates the computation of posterior upper expectations induced by impreci...
In the Bayesian approach to statistical inference, possibly subjective knowledge on model parameters...
AbstractA new approach to constructing generalised probabilities is proposed. It is based on the mod...
Bayesian inference is a method of statistical inference in which all forms of uncertainty are expres...
Bayesian inference and decision making requires elicitation of prior probabilities and sampling dist...
Two major approaches have developed within Bayesian statistics to address uncer-tainty in the prior ...