The concept of safe Bayesian inference [ 4] with learning rates [5 ] has recently sparked a lot of research, e.g. in the context of generalized linear models [ 2]. It is occasionally also referred to as generalized Bayesian inference, e.g. in [2 , page 1] – a fact that should let IP advocates sit up straight and take notice, as this term is commonly used to describe Bayesian updating of credal sets. On this poster, we demonstrate that this reminiscence extends beyond terminology
This report introduces a novel approach to performing inference and learning inDynamic Bayesian Netw...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
We study generalized Bayesian inference under misspecification, i.e. when the model is `wrong but us...
We study generalized Bayesian inference under misspecification, i.e. when the model is ‘wrong but us...
We study generalized Bayesian inference under misspecification, i.e. when the model is `wrong but us...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
Discrete state spaces represent a major computational challenge to statistical inference, since the ...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
The problem of evaluating different learning rules and other statistical estimators is analysed. A n...
Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter ...
How do we compare between hypotheses that are entirely consistent with observations? The marginal li...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
This report introduces a novel approach to performing inference and learning inDynamic Bayesian Netw...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
We study generalized Bayesian inference under misspecification, i.e. when the model is `wrong but us...
We study generalized Bayesian inference under misspecification, i.e. when the model is ‘wrong but us...
We study generalized Bayesian inference under misspecification, i.e. when the model is `wrong but us...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
Discrete state spaces represent a major computational challenge to statistical inference, since the ...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
The problem of evaluating different learning rules and other statistical estimators is analysed. A n...
Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter ...
How do we compare between hypotheses that are entirely consistent with observations? The marginal li...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
This report introduces a novel approach to performing inference and learning inDynamic Bayesian Netw...
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...