Add to ORKGAbstract — Many problems involve joint decision and estima-tion, where qualities of decision and estimation affect each other. This paper proposes an integrated approach based on a new Bayes risk, which is a generalization of those for decision and estimation separately. Theoretical results of the optimal joint decision and estimation that minimizes the new Bayes risk are presented. The power of the new approach is illustrated by applications in target tracking and classification
This dissertation deals with the problem of simultaneously making many (M) binary decisions based on...
A Bayesian decision theoretic approach to directional multiple hypotheses problem
This dissertation mainly consists of three parts. The first part proposes generalized linear minimum...
By reviewing the development of information fusion theory in recent years, this paper analyzes the p...
This thesis treats the problem of joint (simultaneous) detection and estimation which arises when es...
Focussed Bayesian fusion is a local Bayesian fusion technique by that high costs caused by Bayesian ...
The use of discretization in decision analysis allows practitioners to use only a few assessments to...
This book is an introduction to the mathematical analysis of Bayesian decision-making when the state...
This paper proposes a new method for solving Bayesian decision problems. The method con-sists of rep...
An optimal decision framework is proposed for joint detection and decoding when the prior informatio...
Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bay...
We focus on robust Bayesian estimation of the systematic risk of an asset in presence of outlying p...
Recent results of Middleton and Esposito (1968) and Lainiotis (1969) on single-shot joing detection-...
Abstract. Maximum likelihood estimation (MLE) and heuristic predictive estimation (HPE) are two wide...
We first review the concepts fundamental to the statistical inference procedures using nonparametric...
This dissertation deals with the problem of simultaneously making many (M) binary decisions based on...
A Bayesian decision theoretic approach to directional multiple hypotheses problem
This dissertation mainly consists of three parts. The first part proposes generalized linear minimum...
By reviewing the development of information fusion theory in recent years, this paper analyzes the p...
This thesis treats the problem of joint (simultaneous) detection and estimation which arises when es...
Focussed Bayesian fusion is a local Bayesian fusion technique by that high costs caused by Bayesian ...
The use of discretization in decision analysis allows practitioners to use only a few assessments to...
This book is an introduction to the mathematical analysis of Bayesian decision-making when the state...
This paper proposes a new method for solving Bayesian decision problems. The method con-sists of rep...
An optimal decision framework is proposed for joint detection and decoding when the prior informatio...
Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bay...
We focus on robust Bayesian estimation of the systematic risk of an asset in presence of outlying p...
Recent results of Middleton and Esposito (1968) and Lainiotis (1969) on single-shot joing detection-...
Abstract. Maximum likelihood estimation (MLE) and heuristic predictive estimation (HPE) are two wide...
We first review the concepts fundamental to the statistical inference procedures using nonparametric...
This dissertation deals with the problem of simultaneously making many (M) binary decisions based on...
A Bayesian decision theoretic approach to directional multiple hypotheses problem
This dissertation mainly consists of three parts. The first part proposes generalized linear minimum...