UpdateTheta_BrainMap: Parameter Estimates in EM Algorithm for Bayesian brain map

In BayesBrainMap: Estimate Brain Networks and Connectivity with Population-Derived Priors

Parameter Estimates in EM Algorithm for Bayesian brain map

Description

Parameter Estimates in EM Algorithm for Bayesian brain map

Usage

UpdateTheta_BrainMap.spatial(
  prior_mean,
  prior_var,
  meshes,
  BOLD,
  theta,
  C_diag,
  H,
  Hinv,
  s0_vec,
  D,
  Dinv_s0,
  verbose = FALSE,
  return_MAP = FALSE,
  update = c("all", "kappa", "A")
)

UpdateTheta_BrainMap.independent(
  prior_mean,
  prior_var,
  BOLD,
  theta,
  C_diag,
  H,
  Hinv,
  update_nu0sq = TRUE,
  return_MAP = FALSE,
  verbose = TRUE
)

Arguments

prior_mean	(V \times Q matrix) mean maps for each network in prior
prior_var	(V \times Q matrix) between-subject variance maps for each network in prior
meshes	NULL for spatial independence model, otherwise a list of objects of class "BrainMap_mesh" containing the triangular mesh (see make_mesh) for each brain structure.
BOLD	(V \times Q matrix) dimension-reduced fMRI data
theta	(list) current parameter estimates
C_diag	(Qx1) diagonal elements of residual covariance after dimension reduction
H, Hinv	For dimension reduction
s0_vec	Vectorized prior means
D	Sparse diagonal matrix of prior standard deviations
Dinv_s0	The inverse of D times s0_vec
verbose	If TRUE, display progress of algorithm. Default: FALSE.
return_MAP	If TRUE. return the posterior mean and precision of the latent fields instead of the parameter estimates. Default: FALSE.
update	Which parameters to update. Either "all", "A" or "kappa".
update_nu0sq	For non-spatial model: updating nu0sq is recommended if dimension reduction was not performed, and is not recommended if it was.

Value

An updated list of parameter estimates, theta, OR if return_MAP=TRUE, the posterior mean and precision of the latent fields

BayesBrainMap documentation built on Aug. 8, 2025, 7:25 p.m.