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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 ...

Abstract: Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty ...

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, ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

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 ...

For borrowers to receive a clear and detailed guide of all the costs and fees of taking a mortgage, the law requires mortgage companies to provide an estimate of those charges. This was originally ...