This book is an introduction to the mathematical analysis of Bayesian decision-making when the state of the problem is unknown but further data about it can be obtained. The objective of such analysis is to determine the optimal decision or solution that is logically consistent with the preferences of the decision-maker, that can be analyzed using numerical utilities or criteria with the probabilities assigned to the possible state of the problem, such that these probabilities are updated by gathering new information
A textbook and guide to conducting Bayesian decision analysis of sometimes very complex policies and...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
The last five years have seen a surge in interest in the use of techniques from Bayesian decision th...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
Bayesian decision analysis supports principled decision making in complex domains. This textbook tak...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts ...
Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bay...
This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional sta...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
This paper explains why it is important to understand Bayesian techniques and how they are advantage...
A textbook and guide to conducting Bayesian decision analysis of sometimes very complex policies and...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
The last five years have seen a surge in interest in the use of techniques from Bayesian decision th...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
Bayesian decision analysis supports principled decision making in complex domains. This textbook tak...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts ...
Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bay...
This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional sta...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
This paper explains why it is important to understand Bayesian techniques and how they are advantage...
A textbook and guide to conducting Bayesian decision analysis of sometimes very complex policies and...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach...
The last five years have seen a surge in interest in the use of techniques from Bayesian decision th...