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In order to break away from the erroneous assumptions that are so widely popularised about serial criminals and psychological pro ling we need to develop a fully- edged scienti c discipline that will generate processes and theories for contributing to police investigations that have their roots rmly within empirical, scienti c psychology. In order to develop such a discipline one important foundation is to guard against a merely prurient fascination with crimes and criminals. Taking ctional accounts as the starting point for the consideration of psychological contributions to police investigations is therefore fraught with the risk of undermining this important foundation of scienti c work. Yet most academic accounts of such psychological contributions, under the heading of Offender Pro ling , start by citing these works of ction (e.g. Homant and Kennedy, 1998). The particular problem with such ction is that it almost invariably deals with the investigation of Serial Killers. These murderers provide such a simple icon of evil and depravity that they easily generate an apparently worthy foil for the hero of the story, the detective who solves the case. But in order to create an appealing ctional hero who will detect and uncover the villain, the hero s wit and virtues have to be emphasised by contrast with the person who kills over and over again. To emphasise the power of the hero (or more recently heroine) the killer has to be shown not only to
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1.3 ORGANIZATION OF THE BOOK In 2 we present the basic owgraph structures: series, parallel, and loop networks. Using these as building blocks, more complex structures are created. Work with owgraphs requires owgraph algebra, which manipulates probabilities and MGFs for accessing the waiting time distributions of interest. Most statisticians have an aversion to MGFs, and in fact, most students of statistics treat MGFs as an annoyance that is only required knowledge for an exam. Engineers, on the other hand, are quite comfortable with Laplace transforms, which are closely related to MGFs. It turns out that owgraph algebra solves the waiting time distribution problem in the transformed world of MGFs. One should think of the MGF in the same vein as polar coordinates or as any other transformation designed to make solutions more accessible. The simplicity of working with MGFs in the context of modern computing capabilities is made possible by the availability of symbolic algebra packages. Even the person who dreads working with MGFs can have a package do the most dif cult work for them. As such, the latter part of 2 illustrates how to work with owgraphs using the symbolic algebra package maple. Any symbolic algebra package (e.g., mathematica) will suf ce. Detailed examples are presented for many survival analysis applications, including kidney disease progression, AIDS, and cancer. Engineering applications include failure of hydraulic pumps, parts testing, and car stereo manufacturing. Models for recurrent events are also presented for procedures such as endoscopy in survival analysis and repair of components in reliability. In 3 we present tools that convert MGFs into densities. For the simplest of owgraphs this can be done by hand or using maple. For complicated owgraphs we use saddlepoint methods to convert MGFs into densities. Examples originally presented in 2 are more fully developed. Data analysis using owgraphs requires that we model the branches of the owgraph with parametric models. In 4 we present histograms for censored data that help to select such branch models. The chapter begins with background on survivor or reliability functions and hazard functions and then presents methods for censored data. Estimation of survivor or reliability functions using the Kaplan Meier estimator and estimation of the hazard and the cumulative hazard using the Nelson Aalen estimator is also presented. Ultimately, the end results from a owgraph analysis are a Bayes predictive density, predictive CDF, predictive survivor or reliability function, and predictive hazard function. These are derived using the Bayesian prediction methods of
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