eigenLaplace

The eigenvalues of a discrete Laplace matrix with dimension (<code>mm</code>, <code>nn</code>) are
calculated.

A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
To infer which of the captured features are credible, Bayesian analysis is used.
The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
(2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
(3) Posterior credibility analysis of the differences of smooths created.
The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.

Roman Flury

mrbsizeR

Scale Space Multiresolution Analysis of Random Signals

Thimo Schuster

Leena Pasanen

Reinhard Furrer

eigenLaplace function

<dl><dt>mm</dt>
<dd>Number of rows of the discrete Laplace matrix.</dd>
<dt>nn</dt>
<dd>Number of columns of the discrete Laplace matrix.</dd></dl>

Arguments

Generate eigenvalues of discrete Laplace matrix. — eigenLaplace

<dl>

<dt>mm</dt>
<dd>Number of rows of the discrete Laplace matrix.</dd>


<dt>nn</dt>
<dd>Number of columns of the discrete Laplace matrix.</dd>

</dl>

eigenLaplace: Generate eigenvalues of discrete Laplace matrix.

Description

Usage

Value

Arguments

Examples