Denoise: Bayesian wavelet denoising

In jacsor/grove: Wavelet Functional ANOVA Through Markov Groves

Bayesian wavelet denoising

Description

This function carries out Bayesian wavelet denoising using the Normal Inverse Gamma Markov Tree method of Ma and Soriano (2016).

Usage

Denoise(W, alpha = 0.5, nu = 5, n.samples = 500,
  transition.mode = "Markov", method = "Nelder-Mead")

Arguments

W	An object of class DWT.
alpha	Hyperparameter controlling the global smoothness.
nu	Hyperparameter controlling variance heterogeneity. If Inf, then the variance is identical for all nodes.
n.samples	Number of posterior draws.
transition.mode	Type of transition. The two options are Markov or Independent.
method	Method used for find maxmimum of marginal likelihood.

Value

An object of class grove.

References

Ma L. and Soriano J. (2016) Efficient functional ANOVA through wavelet-domain Markov groves. arXiv:1602.03990v2 [stat.ME] (https://arxiv.org/abs/1602.03990v2).

Examples

data <- wavethresh::DJ.EX(n = 512, noisy = TRUE, rsnr = 5)$doppler
W <- DWT(data)
ans <- Denoise(W)

jacsor/grove documentation built on Oct. 12, 2022, 8:33 p.m.