EBSegmentation(data=numeric(), model=1, Kmax = 15, hyper = numeric(),
theta = numeric(), var = numeric(), unif= TRUE)
For the Poisson model, Gamma(1,1) is used. For Negative Binomial model, Jeffreys' prior, Beta(1/2,1/2) is used. For the Normal Homoscedastic, N(0,1) is used for a prior on the mean. Finally, for the Normal Heteroscedastic, the package computes the MAD on the data and fits an inverse-gamma distribution on the result. The parameters are used for the prior on the variance: IG(alpha,beta), and the prior on the mean is N(0,2*beta).
Johnson, Kotz & Kemp: Univariate Discrete Distributions
Hall, Kay & Titterington: Asymptotically optimal difference-based estimation of variance in non-parametric regression
EBS-class
, EBSDistrib
, EBSProfiles
# changes for Poisson model
set.seed(1)
x<-c(rpois(125,1),rpois(100,5),rpois(50,1),rpois(75,5),rpois(50,1))
out <- EBSegmentation(x,model=1,Kmax=20)
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