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Titlebook: Health Status Measurement; A Perspective on Cha Richard G. Brooks Textbook 1995Latest edition Macmillan Publishers Limited 1995 economics.h

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Concluding Remarks,the field. Katz and his colleagues could scarcely have anticipated, as they laboured over the domains of the activities of daily living some 35 years ago that they would be part of a movement which led to such ambitious activities as the calculation of health state utilities and quality-adjusted life-years!
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Richard G. Brookstacle with adjusting the initial stage of the classical . algorithm is dealt via carefully adjusted linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. It remains to properly tune the algorithm and design a mechan
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Richard G. Brooks number of rounds needed to spread the rumour to the entire graph. The Spatial Preferred Attachment (SPA) model is a random graph model that models complex networks: vertices are placed in a metric space, and the link probability depends on the metric distance between vertices, and on their degree.
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Richard G. Brooksg-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how t
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Richard G. BrookseRank in undirected random graphs with expansion property. The Chung-Lu random graph represents an example of such graphs. We show that in the limit, as the size of the graph goes to infinity, PageRank can be represented by a mixture of the restart distribution and the vertex degree distribution.
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Richard G. Brooksg-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how t
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