Journal ArticleDOI
Decision analysis: Perspectives on inference, decision, and experimentation
Ronald A. Howard
- Vol. 58, Iss: 5, pp 632-643
TLDR
This paper illustrates by using a simple coin-tossing example how the new discipline of decision analysis sheds light on the perennial problems of inference, decision, and experimentation.Abstract:
This paper illustrates by using a simple coin-tossing example how the new discipline of decision analysis sheds light on the perennial problems of inference, decision, and experimentation. The inference problem is first discussed from the classical viewpoints of maximum likelihood estimation and hypothesis testing, and then from the viewpoint of subjective probability and Bayesian updating. The problem is next placed in a decision setting to demonstrate how an estimate is related to the nature of the loss structure. Experimental possibilities are evaluated for the case where the size of the experiment must be determined a priori and for the case where experimentation can cease at any point. The decision-analysis philosophy allows consideration of all these problems within one philosophical and methodological framework.read more
Citations
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A Tutorial on Learning Bayesian Networks
TL;DR: In this paper, the authors examine a graphical representation of uncertain knowledge called a Bayesian network, which is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation.
Book ChapterDOI
A tutorial on learning with Bayesian networks
TL;DR: In this article, the authors discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models, including techniques for learning with incomplete data.
Journal ArticleDOI
Bayesian Networks for Data Mining
TL;DR: Methods for constructing Bayesian networks from prior knowledge are discussed and Bayesian statistical methods for using data to improve these models are summarized.
Journal ArticleDOI
Operations for learning with graphical models
TL;DR: In this article, a multidisciplinary review of empirical, statistical learning from a graphical model perspective is presented, including decomposition, differentiation, and manipulation of probability models from the exponential family.
Journal ArticleDOI
A guide to the literature on learning probabilistic networks from data
TL;DR: The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks.
References
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Journal ArticleDOI
Information Value Theory
TL;DR: The theory of the value of information that arises from considering jointly the probabilistic and economic factors that affect decisions is discussed and illustrated and it is found that numerical values can be assigned to the elimination or reduction of any uncertainty.
Journal ArticleDOI
Authors' reply re: BJOG Debate 'Home birth is unsafe'.
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Value of Information Lotteries
TL;DR: In this paper a previous discussion of information value theory is extended to illustrate how the availability of information on the uncertain factors of a problem affects the probability density function of profit, the profit lottery.
Journal ArticleDOI
Bayesian Decision Models for System Engineering
TL;DR: The concept of subjective probability distribution is introduced to permit encoding prior knowledge about the uncertainty in the process and the expected value of clairvoyance is computed as an upper bound to the value of any experimental program.