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Bayesian methods are growing more and more popular, finding new practical applications in the fields of health sciences, engineering, environmental sciences, business and economics and social sciences, among others. This book explores the use of Bayesian analysis in the statistical estimation of the un...
This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be an...
Statistics and ProbabilityStatistics - ApplicationsBayesian Inference is a powerful approach to data analysis that is based almost entirely on probability theory. In this approach, probabilities model {\\it uncertainty} rather than randomness or variability. This thesis is composed of a series of ...
Applications of the theory — to account for rational decision making, scientific confirmation, and qualitative belief — provide other significant points of intra-Bayesian dispute, and we will consider these questions later on. We begin, however, with an outline of the mathematical machinery that ...
The transmuted odd Fréchet-G family of distributions: Theory and applications. Mathematics 2020, 8, 958. [Google Scholar] [CrossRef] Tahir, M.H.; Hussain, M.A.; Cordeiro, G.M.; El-Morshedy, M.; Eliwa, M.S. A New Kumaraswamy Generalized Family of Distributions with Properties, ...
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It is hoped that the approach may throw light on exploring the interplay among time series modelling, Bayesian theory, nonlinear expectations, model uncertainty and their applications. The proposed approach may be related to the Bayesian approach to nonlinear time series and Bayesian econometrics. See...