full_cond: Full conditional for lambda

In BayesCPclust: A Bayesian Approach for Clustering Constant-Wise Change-Point Data

Full conditional for lambda

Description

Full conditional for lambda

Usage

full_cond(kstar, lambda, cluster, al, bl, K, N)

Arguments

kstar	A scalar with the number maximum of change points in all clusters
lambda	A scalar defining the parameter for the Truncate Poisson distribution that controls the number of change points (or its initial values)
cluster	A vector containing the cluster assignments for the data sequences (or its initial values)
al	The hyperparameter value for the shape parameter in the gamma prior for lambda
bl	The hyperparameter value for the scale parameter in the gamma prior for lambda
K	A vector containing the number of change points for each cluster (or its initial values)
N	A scalar representing the number of data sequences

Value

'full_cond' returns a numerical value corresponding to a sample from the full conditional for lambda

Note

This function is used within the Gibbs sampler, it is not expected to be used alone.

Examples

# Using hypothetical values to exemplification purposes
clusters <- c(1,1,2,1,2)
full_cond(kstar = 2, lambda = 3, cluster = clusters, al = 2, bl = 1000, K = c(2, 2), N = 5)

BayesCPclust documentation built on April 4, 2025, 5:19 a.m.