log_posterior_hyper: Conditional Log-Posterior Distributions for MCMC Sampling

In dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models

Conditional Log-Posterior Distributions for MCMC Sampling

Description

These functions compute conditional log-posterior distributions used in a (hierarchical) MCMC sampler.

Usage

log_posterior_hyper(
  phi_j_mat,
  theta_j_mat,
  log_prior_hyper_fun,
  log_prior_lower_fun,
  temperatures,
  suppress_warnings = TRUE
)

log_posterior_lower(
  thetas_one_subj_mat,
  all_phis_mat,
  model_subj,
  log_prior_lower_funs,
  temperatures,
  suppress_warnings = TRUE
)

Arguments

phi_j_mat	a numeric matrix of current group-level parameters for one individual-level parameter. It must be 2 x n_chains and provide the mean and standard deviation; in that order.
theta_j_mat	a numeric matrix of individual-level parameter values across all individuals and chains for one model parameter. Must be n_chains x n_subj.
log_prior_hyper_fun	a function that returns the log-prior density of the hyperparameters. Must be a single function (not a list of functions) and it must accept phi_j_mat as input.
log_prior_lower_fun	a function that returns the log-prior density of individual parameter values given the mean and standard deviation at the group-level (as stored in phi_j_mat). Must be a single function (not a list of functions) and it must accept theta_j_mat as a first argument and input, and the mean and standard deviation with arguments mean and sd (vectorized).
temperatures	a numeric vector of temperature scaling values, one per chain, used when applying tempered inference (e.g., in TIDE).
suppress_warnings	logical, if TRUE, warnings created from log_prior_hyper_fun and log_prior_lower_fun(s) are suppressed. The default is true, because in the beginning of an MCMC sampler implausible proposals are provided which can yield missing values and warnings.
thetas_one_subj_mat	a named matrix of lower-level parameters for a single participant. Each row represents one parameter, and each column one chain.
all_phis_mat	a named matrix of all current group-level parameters. Each mean group-level parameter must be named "M-<param>" and each standard deviation "S-<param>". The ⁠<param>⁠ part must match the individual-level parameters in thetas_one_subj_mat. If this argument is NULL, this indicates that the estimation is done in a non-hierarchical fashion. Each row represents a hyper-parameter, and each column one chain.
model_subj	a drift_dm object, containing an individual's data.
log_prior_lower_funs	a named list of functions, one per parameter stored in thetas_one_subj_mat, returning the log-prior densities. It is assumed that each function can take one type of parameter across chains (i.e., a vector). In the non-hierarchical case, each function can only accept a single vector (for the respective individual-level parameter across chains). In the hierarchical case, each function must also support the arguments mean and sd for vectorized prior computation.

Details

log_posterior_hyper() computes the conditional log-posterior for a group-level hyperparameter matrix phi_j_mat, given the individual-level parameters across subjects theta_j_mat (for one type of model parameter).

log_posterior_lower() computes the conditional log-posterior for an individual participant’s parameter matrix thetas_one_subj_mat, given prior distributions. In the hierarchical setting, the prior distributions are conditioned on the group-level parameters.

Value

A list with two elements:

• posterior_vals, the total log-posterior values (log-likelihood + log-prior) per chain.

• log_like_vals, the log-likelihood components only, per chain.

dRiftDM documentation built on Dec. 1, 2025, 5:08 p.m.