A research team from the Max Planck Institute for Demographic Research (MPIDR) and the Center for Demographic Studies (CED) ...
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 ...
ABSTRACT: Accurate estimation of disease prevalence is crucial for effective public health intervention and resource allocation. Generating data by individual testing methods is often impractical and ...
Researchers developed a fast, accurate method using Bayesian inference to identify electron charge states in quantum dots for quantum computing applications. (Nanowerk News) A research team at Tohoku ...
Abstract: In this paper, we design and analyze distributed Bayesian estimation algorithms for sensor networks. We consider estimation problems, such as cooperative localization and federated learning, ...
The mathematics that enable sensor fusion include probabilistic modeling and statistical estimation using Bayesian inference and techniques like particle filters, Kalman filters, and α-β-γ filters, ...
We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise. We adopt the Bayesian approach by using tensor-product B-splines and endowing the ...
ABSTRACT: In statistical decision theory, the risk function quantifies the average performance of a decision over the sample space. The risk function, which depends on the parameter of the model, is ...
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