Q_IS_mixG: Q Importance Sampling Step

In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion

Q Importance Sampling Step

Description

Q Importance Sampling weighting for mixture Gaussian distributions

Usage

Q_IS_mixG(
  particle_set,
  m,
  time,
  n_comp,
  weights,
  means,
  sds,
  betas,
  precondition_values,
  bounds_multiplier = 1.1,
  seed = NULL,
  n_cores = parallel::detectCores()
)

Arguments

particle_set	particles set prior to Q importance sampling step
m	number of sub-posteriors to combine
time	time T for fusion algorithm
n_comp	integer number of components of mixture Gaussian
weights	vector: weights of mixture Gaussian
means	vector: means of mixture Gaussian
sds	vector: st.devs of mixture Gaussian
betas	vector of length c, where betas[c] is the inverse temperature value for c-th posterior
precondition_values	vector of length m, where precondition_values[c] is the precondition value for sub-posterior c
bounds_multiplier	scalar value to multiply bounds by (should greater than or equal to 1)
seed	seed number - default is NULL, meaning there is no seed
n_cores	number of cores to use

Value

An updated particle set

rchan26/hierarchicalFusion documentation built on Sept. 11, 2022, 10:30 p.m.