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  1. Shoutir Kishore Chatterjee (2003). Statistical Thought: A Perspective and History. OUP Oxford.
    In this unique monograph, based on years of extensive work, Chatterjee presents the historical evolution of statistical thought from the perspective of various approaches to statistical induction. Developments in statistical concepts and theories are discussed alongside philosophical ideas on the ways we learn from experience. -/- Suitable for researchers, lecturers and students in statistics and the history of science this book is aimed at those who have had some exposure to statistical theory. It is also useful to logicians and philosophers (...)
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  2. James Franklin & Scott Sisson, Assessment of Strategies for Evaluating Extreme Risks. Australian Centre of Excellence for Risk Analysis Reports.
    The report begins by outlining several case studies with varying levels of data, examining the role for extreme event risk analysis. The case studies include BA’s analysis of fire blight and New Zealand apples, bank operational risk and several technical failures. The report then surveys recent developments in methods relevant to evaluating extreme risks and evaluates their properties. These include methods for fraud detection in banks, formal extreme value theory, Bayesian approaches, qualitative reasoning, and adversary and advocacy models. The document (...)
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  3. Niki Pfeifer & G. D. Kleiter (2006). Towards a Probability Logic Based on Statistical Reasoning. In Proceedings of the 11 T H Ipmu International Conference (Information Processing and Management of Uncertainty in Knowledge-Based Systems). 2308--2315.
    Logical argument forms are investigated by second order probability density functions. When the premises are expressed by beta distributions, the conclusions usually are mixtures of beta distributions. If the shape parameters of the distributions are assumed to be additive (natural sampling), then the lower and upper bounds of the mixing distributions (P´olya-Eggenberger distributions) are parallel to the corresponding lower and upper probabilities in conditional probability logic.
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