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Safety --- Probability distribution functions --- Cost engineering
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Statistics. --- Probability distribution functions --- Probability theory
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Probability theory --- Probability distribution functions --- Normal density functions
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Probabilités --- Probabilities. --- Calcul d'erreur --- Probability distribution functions
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Probabilités --- Probabilities. --- Calcul d'erreur --- Probability distribution functions
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Rain and rainfall. --- Watersheds. --- Floods --- Storms --- Flood forecasting --- Stochastic processes --- Probability distribution functions
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Precipitation (Meteorology) --- Runoff. --- Stream flow --- Probability distribution functions --- Fitting --- Chi square test --- Flow distribution
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Mathematical optimization. --- Mathematical models. --- Probability distribution functions --- Experimental data --- Computerized simulation --- Computer programs --- Executive routines
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The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.
Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Physical Sciences --- Engineering and Technology --- Mathematics --- Statistics --- Probability Distribution
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