Description Usage Arguments Value References See Also
Estimate boundaries in a continuous intensity image.
1 2  BayesBDnormal(obs, inimean, nrun, nburn, J, ordering_mu,
ordering_sigma, mask, slice, outputAll)

obs 
The noisy observation which is a list with the following required elements:

inimean 
a constant to specify the initial mean functions in the Bayesian estimation. 
nrun 
the number of MCMC samples to keep for estimation. 
nburn 
the number of initial MCMC samples to discard. 
J 
truncation number of the Gaussian process kernel. The number of eigenfunctions is 2J + 1. 
ordering_mu 
Indicates which Gaussian distribution has larger mean intensity: "I", the Gaussian distribution inside the boundary; "O", the Gaussian distribution outside the boundary; "N", no ordering information is available. 
ordering_sigma 
Indicates which Gaussian distribution has larger intensity variance: "I", the Gaussian distribution inside the boundary; "O", the Gaussian distribution outside the boundary; "N", no ordering information is available. 
mask 
Logical vector (same length as obs$intensity) to indicate region of interest. Should this data point be included in the analysis? 
slice 
boolean where TRUE means that slice sampling will be used to sample Fourier basis function coefficients and FALSE means that MetropolisHastings will be used instead. 
outputAll 
boolean controlling the amount of output produced, see value below. 
If outputAll is FALSE,
estimate 
Posterior mean estimate of image boundary at theta values. 
theta 
A grid of 200 values on [0,2π] at which to retrun the estimated boundary. 
lower, upper 
The lower and upper bounds of a 95\% uniform credible band for the image boundary. 
If outputAll is TRUE, same as above, and additionally,
musig.smp 
posterior samples of μ_1, μ_2, σ_1, and σ_2. 
coef.smp 
posterior samples of Fourier basis function coefficients. 
Li, M. and Ghosal, S.(2015) "Bayesian Detection of Image Boundaries." arXiv 1508.05847.
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