Optimal parameter choice for error minimization in bivariate histograms

Parameter choice methods using minimization schemes

Regularization is typically based on the choice of some parametric family of nearby solutions, and the choice of this family is a task in itself. Then, a suitable parameter must be chosen in order to find an approximation of good quality. We focus on the second task. There exist deterministic and stochasticmodels for describing noise and solutions in inverse problems.Wewill establish a unified ...

Choice of Optimal Error-correcting Code for Physical Unclonable Functions

This thesis explores error-correcting codes which can be used in physical unclonable function (PUF) applications. We investigate linear block codes and concatenated codes, which are traditionally used with PUFs, and compare them to convolutional codes using the criteria of error correction capability, decoder hardware requirements, flexibility of code parameters, and code rate. The application ...

Bayes Parameter Estimation for the Bivariate

Error bounds for bivariate piecewise hermite interpolation

Optimal Data-Based Binning for Histograms

Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. In this paper we introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. Using the Bayesian framework, we der...