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eBook Modeling in Medical Decision Making: A Bayesian Approach download

by Giovanni Parmigiani

eBook Modeling in Medical Decision Making: A Bayesian Approach download ISBN: 0471986089
Author: Giovanni Parmigiani
Publisher: Wiley; 1 edition (March 1, 2002)
Language: English
Pages: 280
ePub: 1139 kb
Fb2: 1288 kb
Rating: 4.1
Other formats: mobi lrf docx doc
Category: Different
Subcategory: Medicine and Health Sciences

The decision trees have been constructed by using the modified Quinlan's approach based on choosing relevant attributes according to their informativity. In such a quantitative book, there should be balanced presentation of concepts in English and algebra, with clear explanations of the meanings of all symbols and.

Автор: Giovanni Parmigiani Название: Modeling in Medical Decision Making: A Bayesian Approach . Описание: This title confidently puts forward a practical, new approach to decision making in an uncertain business world.

Описание: This title confidently puts forward a practical, new approach to decision making in an uncertain business world.

Medical decision making has evolved in recent years, . .Goodreads helps you keep track of books you want to read. Start by marking Modeling in Medical Decision Making: A Bayesian Approach as Want to Read: Want to Read savin. ant to Read.

by Giovanni Parmigiani (Author) . This is a nice readable book about decision theory from the Bayesian perspective. ISBN-13: 978-0471986089. with considerable clarity.

Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based . Giovanni Parmigiani is the author of Modeling in Medical Decision Making: A Bayesian Approach, published by Wiley.

Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making.

Medical decision making has evolved in recent years, as.Case studies include simplified versions of the analysis, to approach complex modelling in stages. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation–based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. Case studies include simplified versions of the analysis, to approach complex modelling in stages OZON.

Woodworth, George, 2007. Modeling in Medical Decision Making: A Bayesian Approach, Giovanni Parmigiani," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 387-388, March. Handle: RePEc:bes:jnlasa:v:102:y:2007:p:387-388. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

Keywords: medical decision making, 98608, Approach Parmigiani, Isbn 0, Sons Ww. ileyeurope. For questions or feedback, please reach us at support at scilit.

Decision Making, Computer-Assisted. 61. 27 P255 (Browse Shelf).

Farmer -Ability -Personality -Goals -Experience -Psychology -Peers -etc -etc A Bayesian approach Our questio.

Medical decision making has evolved in recent years, as morecomplex problems are being faced and addressed based onincreasingly large amounts of data. In parallel, advances incomputing have led to a host of new and powerful statistical toolsto support decision making. Simulation-based Bayesian methods areespecially promising, as they provide a unified framework for datacollection, inference, and decision making. In addition, thesemethods are simple to interpret, and can help to address the mostpressing practical and ethical concerns arising in medical decisionmaking.* Provides an overview of the necessary methodological background,including Bayesian inference, Monte Carlo simulation, and utilitytheory.* Driven by three real applications, presented as extensivelydetailed case studies.* Case studies include simplified versions of the analysis, toapproach complex modelling in stages.* Features coverage of meta-analysis, decision analysis, andcomprehensive decision modeling.* Accessible to readers with only a basic statisticalknowledge.Primarily aimed at students and practitioners of biostatistics, thebook will also appeal to those working in statistics, medicalinformatics, evidence-based medicine, health economics, healthservices research, and health policy.