From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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It is important that the medical profession play a significant role in critically evaluating the use of diagnostic procedures and therapies as they are introduced in the detection, management,

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ACC/AHA 2006 Guidelines for the Management of Patients With Valvular Heart Disease

This book presents the definitive exposition of 'prospect theory', a compelling alternative to the classical utility theory of choice. Building on the 1982 volume, Judgement Under Uncertainty, this book brings together seminal papers on prospect theory from economists, decision theorists, and psychologists, including the work of the late Amos Tversky, whose contributions are collected here for the first time. While remaining within a rational choice framework, prospect theory delivers more accurate, empirically verified predictions in key test cases, as well as helping to explain many complex, real-world puzzles. In this volume, it is brought to bear on phenomena as diverse as the principles of legal compensation, the equity premium puzzle in financial markets, and the number of hours that New York cab drivers choose to drive on rainy days. Theoretically elegant and empirically robust, this volume shows how prospect theory has matured into a new science of decision making.

Choices, Values, and Frames

Users of clinical practice guidelines and other recommendations need to know how much confidence they can place in the recommendations Systematic and explicit methods of making judgments can reduce errors and improve communication We have developed a system for grading the quality of evidence and the strength of recommendations that can be applied across a wide range of interventions and contexts In this article we present a summary of our approach from the perspective of a guideline user Judgments about the strength of a recommendation require consideration of the balance between benefits and harms, the quality of the evidence, translation of the evidence into specific circumstances, and the certainty of the baseline risk It is also important to consider costs (resource utilisation) before making a recommendation Inconsistencies among systems for grading the quality of evidence and the strength of recommendations reduce their potential to facilitate critical appraisal and improve communication of these judgments Our system for guiding these complex judgments balances the need for simplicity with the need for full and transparent consideration of all important issues

/pdf/grading-quality-of-evidence-and-strength-of-recommendations-4juchvwoau.pdf

Grading quality of evidence and strength of recommendations.

We discuss the cognitive and the psy- chophysical determinants of choice in risky and risk- less contexts. The psychophysics of value induce risk aversion in the domain of gains and risk seeking in the domain of losses. The psychophysics of chance induce overweighting of sure things and of improbable events, relative to events of moderate probability. De- cision problems can be described or framed in multiple ways that give rise to different preferences, contrary to the invariance criterion of rational choice. The pro- cess of mental accounting, in which people organize the outcomes of transactions, explains some anomalies of consumer behavior. In particular, the acceptability of an option can depend on whether a negative outcome is evaluated as a cost or as an uncompensated loss. The relation between decision values and experience values is discussed. Making decisions is like speaking prose—people do it all the time, knowingly or unknowingly. It is hardly surprising, then, that the topic of decision making is shared by many disciplines, from mathematics and statistics, through economics and political science, to sociology and psychology. The study of decisions ad- dresses both normative and descriptive questions. The normative analysis is concerned with the nature of rationality and the logic of decision making. The de- scriptive analysis, in contrast, is concerned with peo- ple's beliefs and preferences as they are, not as they should be. The tension between normative and de- scriptive considerations characterizes much of the study of judgment and choice. Analyses of decision making commonly distin- guish risky and riskless choices. The paradigmatic example of decision under risk is the acceptability of a gamble that yields monetary outcomes with specified probabilities. A typical riskless decision concerns the acceptability of a transaction in which a good or a service is exchanged for money or labor. In the first part of this article we present an analysis of the cog- nitive and psychophysical factors that determine the value of risky prospects. In the second part we extend this analysis to transactions and trades. Risky Choice Risky choices, such as whether or not to take an umbrella and whether or not to go to war, are made without advance knowledge of their consequences. Because the consequences of such actions depend on uncertain events such as the weather or the opponent's resolve, the choice of an act may be construed as the acceptance of a gamble that can yield various out- comes with different probabilities. It is therefore nat- ural that the study of decision making under risk has focused on choices between simple gambles with monetary outcomes and specified probabilities, in the hope that these simple problems will reveal basic at- titudes toward risk and value. We shall sketch an approach to risky choice that

http://psycnet.apa.org/doi/10.1037/0003-066X.39.4.341

The rapid generation of strength-interval curves under computer control.

Individual patients’ values and preferences are critical to patient-centered decisions. When dealing with health outcomes on an interval scale, where many intermediate outcomes must be considered, it can be helpful to encourage the patient decision maker to consider what functional form may be specified and parameterized for a given outcome attribute. This assessment problem is described in Keeney and Raiffa’s classic book. 1 For simplicity, let us restrict ourselves to unidimensional settings. We are asked first to specify an attitude toward risk (risk neutral, risk averse, or risk seeking) and then subclassify attitudes depending on whether the risk attitude is constant, absolutely decreasing, or relatively decreasing, in the case of the most common attitude, risk aversion. These questions then often specifyafunctionalformforthepatient’sutilityfunction,whichcanbeparameterizedonthebasisofalimited number of responses from the patient decision maker. This approach tends to consider just risk aversion, the curvature of the utility function as determined by the ratio of its second to its first derivative. That curvature can be quantified by the Pratt-Arrow measure of risk aversion, 2 u 2 /u 1 , although other measures, such as the stronger Ross measure of risk aversion, 3–6 are sometimes used. We use the notation u i to denote the ith derivative of u(x), d i u(x)/dx i . For utilitydistribution functions,onecanconsider either utility density functions (udfs) or their integrals, cumulative utility functions (cufs), analogous toprobabilitydensity functions (pdfs) and cumulative distribution functions (cdfs). For density functions, shape can be described by the function’s moments (e.g., 0-mean, 1-slope, 2-variance, 3-skewness, 4-kurotosis,andhigherunnamedmoments).Normatively, utilities with positive skewness are preferred by individuals whose utility function has u 3 . 0, whereas low kurtosis should be preferred by individuals whose utility function has u 4 \0. 7

https://journals.sagepub.com/doi/pdf/10.1177/0272989X13495714

Moments when utilities are functional.

This paper examines the implications of occult fecal blood loss in patients taking aspirin (at least 2 grams daily). Although such patients do have a somewhat higher probability of colonic carcinoma than do members of the general population, their risk is far lower than that of patients who have gastrointestinal blood loss when not taking aspirin. This difference in risk exists because aspirin itself can provoke occult blood loss in stool. Patients who manifest gastrointestinal blood loss while taking aspirin can be separated into two groups, based on whether or not that blood loss continues after aspirin is discontinued. Although patients who continue to bleed are at high risk for colonic carcinoma, those who cease having any blood loss are at lower risk than are members of the general population. Further diagnostic studies to detect colonic carcinoma should be pursued in the former group, but not in the latter, low-risk group.

Evaluation for colon cancer in patients with occult fecal blood loss while taking aspirin: a Bayesian viewpoint.

The Practical Use of Decision Analysis in Patient Care

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Stephen G. Pauker

Papers

The rapid generation of strength-interval curves under computer control.

Moments when utilities are functional.

Evaluation for colon cancer in patients with occult fecal blood loss while taking aspirin: a Bayesian viewpoint.

The Practical Use of Decision Analysis in Patient Care

The clinical interpretation of research.