Discover how credibility theory helps actuaries use historical data to estimate risks and set insurance premiums; learn how ...
These open-source MMM tools solve different measurement problems, from budget optimization to forecasting and preprocessing.
How modern mathematics helps policymakers and insurers connect the dots of the country’s fragmented demographic data ...
Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
Sampling is an essential step in estimating a parameter: thus, cost and time associated to this step should be minimized. Sequential sampling is characterized by using samples of variable sizes given ...
Abstract: The Bayesian Cramér-Rao bound (CRB) provides a lower bound on the mean square error of any Bayesian estimator under mild regularity conditions. It can be ...
Mathematics Department, Egerton University, Njoro Nakuru, Kenya. Bayesian techniques have been applied in many epidemiological settings, such as disease monitoring, outbreak simulation, and prevalence ...
Abstract: Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often ...
Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
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