bmdl.MH: Metropolis-Hastings Algorithms for Stoachstic Model Search

In yingboli/BayesMDL: Bayesian MDL for Multiple Changepoint Detection

Description

Two proposals: the function bmdl.MH.flip proposes to flip a random dimension, and the function bmdl.MH.swap propose to swap between a randomly chosen zero and a randomly chosen one.

Usage

bmdl.MH.flip(X, month, current, meta, p, fit, penalty, nu, a, b1, b2, period)

bmdl.MH.swap(X, month, current, meta, p, fit, penalty, nu, a, b1, b2, period)

Arguments

X	A vector of length N, observed time series data.
month	A vector of length N, month indicators. Takes value in 1, 2, ..., 12 for monthly data, or 1 for annual data.
current	A list of current changepoint model. Specifically, list(eta, inference, change.eta), where inference is the output of the function bmdl.eta.
meta	A 0-1 indicator vector of length N, metadata indicator. The first p elemenet are always 0.
p	The order of the AR process.
fit	For likelihood calculation, 'marlik' for marginal likelihood, or 'lik' for likelihood. Note that the 'lik' option already includes the two-part MDL of mu.
penalty	For penalty function calculation, 'bmdl' for Beta-Binomial prior, or 'mdl' for MDL.
nu	Prior variance scale of mu; only used if fit == 'marlik'.
a	The first and second parameters in the Beta-Binomial prior; only used if penalty == 'bmdl'.
b1	The b parameter in the Beta-Binomial prior of η, for un-documented times (i.e., times not in metadata). Default value is 239 for monthly data, and 19 for annual data.
b2	The b parameter in the Beta-Binomial prior of η, for documented times (i.e., times in metadata). Default value is 47 for monthly data, and 3 for annual data.
period	Period of the seasonal cycle, 12 for monthly data, or 1 for annual data.

Value

A list of current changepoint model. Specifically, list(eta, inference, change.eta), where inference is the output of the function bmdl.eta.

yingboli/BayesMDL documentation built on May 29, 2019, 12:18 p.m.