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CVI implementation for one set of initial parameters

Source: R/cvi_singlerun.R
run_single.Rd

CVI implementation for one set of initial parameters

Usage

run_single(
  config,
  X,
  N,
  D,
  T0,
  prior_shape_alpha,
  prior_rate_alpha,
  post_shape_alpha,
  post_rate_alpha,
  prior_mean_eta,
  post_mean_eta,
  fixed_variance,
  covariance_type,
  cluster_specific_covariance,
  variance_prior_type,
  maxit,
  varargs
)

Arguments

config

List of inputs that are generated if not user-provided

X

the data matrix

N

samples of X

D

dimensions of X

T0

variational clusters

prior_shape_alpha

shape parameter of Gamma prior for the DP concentration parameter alpha. Default is 0.001

prior_rate_alpha

rate parameter of Gamma prior for the DP concentration parameter alpha. Default is 0.001

post_shape_alpha

initial value for posterior update of shape parameter for alpha. Default is 0.001

post_rate_alpha

initial value for posterior update of ratee parameter for alpha. Default is 0.001

prior_mean_eta

mean vector of MVN prior for the DP mean parameters. Default is zero vector

post_mean_eta

initial value of posterior update for the DP mean parameter

fixed_variance

covariance matrix of the data is considered known (fixed) or unknown.

covariance_type

covariance matrix is considered diagonal or full.

cluster_specific_covariance

covariance matrix is specific to a cluster allocation or it is same over all cluster choices.

variance_prior_type

For unknown and full covariance matrix, choice of matrix prior is either Inverse-Wishart ('IW') or Cholesky-decomposed ('decomposed'). For unknown, full and cluster-specific covariance matrix, choice of matrix prior is either Inverse-Wishart ('IW'), element-wise Gamma and Laplace distributed ('sparse') or element-wise Gamma and Normal distributed ('off-diagonal normal')

maxit

Maximum number of iterations for variational updates

varargs

List of case specific parameters

Value

a list with the following elements:

  • alpha: posterior DP concentration parameter

  • Cluster number: number of clusters from posterior probability allocation matrix

  • Cluster Proportion: cluster proportions from posterior probability allocation matrix

  • log Probability matrix: log of posterior probability allocation matrix

  • ELBO: Optimisation of the ELBO function

  • Iterations: Number of iterations required for convergence