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This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...
Heterogeneity among experimental units can introduce experimental errors, necessitating the use of techniques that enhance statistical inferences to address this issue. One effective approach is ...
Addressing a variety of questions within Earth science disciplines entails the inference of the spatiotemporal distribution of parameters of interest based on observations of related quantities. Such ...
A research team has developed a new technique to rapidly and accurately determine the charge state of electrons confined in semiconductor quantum dots -- fundamental components of quantum computing ...
The folder contains examples and codes developed in the Willy Mutchler lecture's at the Tübingen University . The course deals with estimation of SVAR and DSGE models ...
This paper studies nonparametric estimation of parameters of multivariate Hawkes processes. We consider the Bayesian setting and derive posterior concentration rates. First, rates are derived for ...
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, ...
Abstract: This work studies an information-theoretic performance limit of an integrated sensing and communication (ISAC) system where the goal of sensing is to ...
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 ...
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 ...
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