This paper addresses the problem that Bayesian statistical inference cannot accommodate theory change, and proposes a framework for dealing with such changes. It first presents a scheme for generating predictions from observations by means of hypotheses. An example shows how the hypotheses represent the theoretical structure underlying the scheme. This is followed by an example of a change of hypotheses. The paper then presents a general framework for hypotheses change, and proposes the minimization of the distance between hypotheses as a rationality criterion. Finally the paper discusses the import of this for Bayesian statistical inference
Many philosophers have claimed that Bayesianism can provide a simple justification for hypothetico-d...
The Bayesian approach to probability and statistics is described, a brief history of Bayesianism is ...
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue t...
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory chang...
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory chang...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
Our scientific theories, like our cognitive structures in general, consist of propositions linked by...
Scientists and Bayesian statisticians often study hypotheses that they know to be false. This create...
This paper discusses the role of theoretical notions in making predictions and evaluating statistica...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
Scientists and Bayesian statisticians often study hypotheses that they know to be false. This create...
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular a...
We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian parad...
We present a conservative extension of a Bayesian account of confirmation that can deal with the pro...
We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a norma...
Many philosophers have claimed that Bayesianism can provide a simple justification for hypothetico-d...
The Bayesian approach to probability and statistics is described, a brief history of Bayesianism is ...
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue t...
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory chang...
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory chang...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
Our scientific theories, like our cognitive structures in general, consist of propositions linked by...
Scientists and Bayesian statisticians often study hypotheses that they know to be false. This create...
This paper discusses the role of theoretical notions in making predictions and evaluating statistica...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
Scientists and Bayesian statisticians often study hypotheses that they know to be false. This create...
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular a...
We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian parad...
We present a conservative extension of a Bayesian account of confirmation that can deal with the pro...
We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a norma...
Many philosophers have claimed that Bayesianism can provide a simple justification for hypothetico-d...
The Bayesian approach to probability and statistics is described, a brief history of Bayesianism is ...
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue t...