This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critic
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
ABSTRACT: Bayesianism and Inference to the best explanation (IBE) are two different models of infere...
16 pages, 2 figures, 2 tables, chapter of the contributed volume "Bayesian Methods and Expert Elicit...
Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges th...
Bayesian decision analysis supports principled decision making in complex domains. This textbook tak...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
The Bayesian logic of probability, evidence and decision is the presumed rule of reasoning in analyt...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
This paper discusses the role of theoretical notions in making predictions and evaluating statistica...
This book is an introduction to the mathematical analysis of Bayesian decision-making when the state...
THE MATHEMATICAL THEORY OF PROBABILITY, UNDER THE SO-CALLED BAYESIAN OR SUBJECTI...
Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges th...
In this thesis we present a review of the Bayesian approach to Statistical Inference. In Chapter One...
We present here a Bayesian framework of risk perception. This framework encompasses plausibility jud...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
ABSTRACT: Bayesianism and Inference to the best explanation (IBE) are two different models of infere...
16 pages, 2 figures, 2 tables, chapter of the contributed volume "Bayesian Methods and Expert Elicit...
Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges th...
Bayesian decision analysis supports principled decision making in complex domains. This textbook tak...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
The Bayesian logic of probability, evidence and decision is the presumed rule of reasoning in analyt...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
This paper discusses the role of theoretical notions in making predictions and evaluating statistica...
This book is an introduction to the mathematical analysis of Bayesian decision-making when the state...
THE MATHEMATICAL THEORY OF PROBABILITY, UNDER THE SO-CALLED BAYESIAN OR SUBJECTI...
Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges th...
In this thesis we present a review of the Bayesian approach to Statistical Inference. In Chapter One...
We present here a Bayesian framework of risk perception. This framework encompasses plausibility jud...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
ABSTRACT: Bayesianism and Inference to the best explanation (IBE) are two different models of infere...
16 pages, 2 figures, 2 tables, chapter of the contributed volume "Bayesian Methods and Expert Elicit...