Add to ORKGA large body of work has demonstrated the utility of the Bayesian framework for capturing inference in both specialist and everyday contexts. However, the central tool of the framework, conditionalization via Bayes’ rule, does not apply directly to a common type of learning: the acquisition of conditional information. How should an agent change her beliefs on learning that “If A, then C”? This issue, which is central to both reasoning and argumentation, has recently prompted considerable research interest. In this paper, we critique a prominent proposal and provide a new, alternative, answer
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
This paper characterizes different belief revision rules in a unified framework: Bayesian revision u...
At the heart of the Bayesianism is a rule, Conditionalization, which tells us how to update our beli...
A large body of work has demonstrated the utility of the Bayesian framework for capturing inference ...
A large body of work has demonstrated the utility of the Bayesian framework for capturing inference ...
A large body of work has demonstrated the utility of the Bayesian framework for capturing inference ...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
This paper characterizes different belief revision rules in a unified framework: Bayesian revision u...
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
This paper characterizes different belief revision rules in a unified framework: Bayesian revision u...
At the heart of the Bayesianism is a rule, Conditionalization, which tells us how to update our beli...
A large body of work has demonstrated the utility of the Bayesian framework for capturing inference ...
A large body of work has demonstrated the utility of the Bayesian framework for capturing inference ...
A large body of work has demonstrated the utility of the Bayesian framework for capturing inference ...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
This paper characterizes different belief revision rules in a unified framework: Bayesian revision u...
It is a commonplace in epistemology that credences should equal known chances. It is less clear, how...
This paper characterizes different belief revision rules in a unified framework: Bayesian revision u...
At the heart of the Bayesianism is a rule, Conditionalization, which tells us how to update our beli...