Spencer Graves

Access to Wholistic and Productive Living Institute

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J. O. Ramsay

McGill University

Giles Hooker

Cornell University

Søren Bisgaard

University of Massachusetts Amherst

John Van Gilder

Convergent Science (United States)

John V. James

Institute of Nuclear Physics, Polish Academy of Sciences

Cuiping Wu

Harbin Medical University

George E. P. Box

HM Prison and Probation Service

Murat Külahçı

Luleå University of Technology

Hal Zatorski

Daimler (United States)

Ken Marko

Cooperative Institutions

PDF Solutions (United States)

University of Wisconsin–Madison

Daimler (Germany)

Solutions Inc. (Japan)

University of Massachusetts Amherst

University of Amsterdam

University of Pennsylvania

Rutgers, The State University of New Jersey

Technion – Israel Institute of Technology

University of Minnesota

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Bayes’ Rule of Information and Monitoring in Manufacturing Integrated Circuits

Chapman and Hall/CRC eBooks (2006)

Spencer Graves

This chapter describes (a ) how Bayes' theorem implies the addition of (observed) information and (b) the use of this property to easily compute posteriors: Defining "score" and "observed information" via derivatives of the log densities, the posterior score is the prior score plus the score from the data, and observed information similarly adds. These facts provide simple derivations of many Bayesian results, especially for normal approximations. Other tools can then be used to evaluate the adequacy of these approximations. Even when, for example, a normal posterior is not sufficiently accurate for direct use, it can still be used as part of an improved solution obtained via adaptive Gauss-Hermite quadrature or importance sampling in Monte Carlo integration, for example.

10.1201/9781420010701-12

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sos: Searching Help Pages of R Packages

The R Journal (2009)

Spencer Graves Sundar Dorai‐Raj Romain François

The sos package provides a means to quickly and flexibly search the help pages of contributed packages, finding functions and datasets in seconds or minutes that could not be found in hours or days by any other means we know.Its findFn function accesses Jonathan Baron's R Site Search database and returns the matches in a data frame of class "findFn", which can be further manipulated by other sos functions to produce, for example, an Excel file that starts with a summary sheet that makes it relatively easy to prioritize alternative packages for further study.As such, it provides a very powerful way to do a literature search for functions and packages relevant to a particular topic of interest and could become virtually mandatory for authors of new packages or papers in publications such as The R Journal and the Journal of Statistical Software.

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10.32614/rj-2009-017

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Accelerated testing of on‐board diagnostics

Quality and Reliability Engineering International (2006)

Spencer Graves Søren Bisgaard Murat Külahçı John Van Gilder John V. James

Abstract Modern products frequently feature monitors designed to detect actual or impending malfunctions. False alarms (Type I errors) or excessive delays in detecting real malfunctions (Type II errors) can seriously reduce monitor utility. Sound engineering practice includes physical evaluation of error rates. Type II error rates are relatively easy to evaluate empirically. However, adequate evaluation of a low Type I error rate is difficult without using accelerated testing concepts, inducing false alarms using artificially low thresholds and then selecting production thresholds by appropriate extrapolation, as outlined here. This acceleration methodology allows for informed determination of detection thresholds and confidence in monitor performance with substantial reductions over current alternatives in time and cost required for monitor development. Copyright © 2006 John Wiley & Sons, Ltd.

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10.1002/qre.784

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Better records: first step to better quality.

PubMed (1971)

Spencer Graves

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On the Neyman–Pearson Theory of Testing

The British Journal for the Philosophy of Science (1978)