plot_changepoint_posterior: Plot the empirical posterior distribution of the...

In BCT: Bayesian Context Trees for Discrete Time Series

Plot the empirical posterior distribution of the change-points.

Description

This function plots the empirical posterior distribution of the change-points.

Usage

plot_changepoint_posterior(res, burn)

Arguments

res	the output obtained from the Metropolis-Hastings algorithms.
burn	the proportion of the samples discarded as burn-in.

Value

returns plot of the empirical posterior of the number of change-points (if the results from the infer_unknown_changepoints function were used).

returns plot of the empirical posterior of the change-points.

See Also

infer_unknown_changepoints, infer_fixed_changepoints

Examples

# Use as an example the el_nino dataset.
# Run the function with l_max = 3 change-points, a maximum depth of 5 and the [0, 1] alphabet.
# The sampler is run for 100 iterations

res_unknown <- infer_unknown_changepoints(el_nino, 3, 5, c("01"), 100, fileName = NULL)

# Plot the posterior distribution of the locations and the posterior of the number of change-points.

plot_changepoint_posterior(res_unknown, 0.2)

# This function can be also used with the infer_fixed_changepoints.
# Assume l = 2.

res_fixed <- infer_fixed_changepoints(el_nino, 2, 5, c("01"), 100, fileName = NULL)

# Now, the function will only output the posterior distribution of the change-points 
# (the number is fixed).

plot_changepoint_posterior(res_fixed, 0.2)

BCT documentation built on May 12, 2022, 5:06 p.m.