Usage Arguments Value References Examples
View source: R/sourceFunction.R
1  BayesBD.binary(obs, ini.mean = 0.4, n.run = 10000, n.burn = 1000, J = 10)

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

ini.mean 
a constant to specify the initial mean functions in the Bayesian estimation, defaulted as 0.4. 
n.run 
number of MCMC iterations. 
n.burn 
number of burnin in the MCMC sampler. 
J 
truncation number of the Gaussian process kernel. The number of eigenfunctions is 2J + 1. 
Posterior samples of all parameters.
Li, M. and Ghosal, S.(2015) "Bayesian Detection of Image Boundaries." arXiv preprint arXiv:1508.05847.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  set.seed(2015)
# ellipse boundary
gamma.fun = ellipse(a = 0.35, b = 0.25)
obs = par2obs(m = 100, pi.in = 0.5, pi.out = 0.2, design = 'J', gamma.fun)
## Not run:
# it takes around 7min if runs 10000 iterations: saved in 'data.Rdata'
BayesEst = BayesBD.binary(obs, n.run = 10000, n.burn = 1000)
## End(Not run)
data(data)
# visualize the estimates
theta.plot = seq(from = 0, to = 2*pi, length.out = 200)
gamma.hat.theta = BayesEst$gamma.hat(theta.plot)
## plotting utilities
require(plotrix)
my.radial < function(r, theta, ...){
radial.plot(c(r[order(theta)]), c(theta[order(theta)]),
rp.type = "p", show.grid.label = TRUE, radial.lim = c(0, 0.5),
...)
}
# rotate a matrix
rotate < function(x) t(apply(x, 2, rev)) # rotate closewise by 90 degrees
par(mfrow = c(1, 2))
# rotate & image it  square (asp = 1)
image(rotate(obs$intensity), axes = FALSE, asp = 1, main = 'observation')
my.radial(gamma.fun(theta.plot), theta.plot, line.col = 1, lty = 2, lwd = 2,
main = 'Estimated boundary vs. True', show.grid = FALSE)
my.radial(gamma.hat.theta, theta.plot, add = TRUE,
line.col = 'red', lty = 2, lwd = 2, show.grid = FALSE)

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