posterior: Calculate the log-posterior during inference

In kieranrcampbell/mfa: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations

Calculate the log-posterior during inference

Description

Calculate the log-posterior during inference

Usage

posterior(y, c, k, pst, tau, gamma, theta, eta, chi, w, tau_c, r, alpha, beta,
  theta_tilde, eta_tilde, tau_theta, tau_eta, alpha_chi, beta_chi,
  zero_inflation = FALSE, lambda = NULL)

Arguments

y	Cell-by-gene gene expression matrix
c	Factor loading parameter
k	Factor loading parameter
pst	Pseudotime vector
tau	Precision parameter
gamma	Branch responsibility parameter
theta	Factor loading parameter
eta	Factor loading parameter
chi	ARD-like precision
w	Branch responsibility prior (simplex)
tau_c	Hyperparameter
r	Hyperparameter
alpha	Hyperparamter
beta	Hyperparameter
theta_tilde	Hyperparameter
eta_tilde	Hyperparameter
tau_theta	Hyperparameter
tau_eta	Hyperparameter
alpha_chi	Hyperparameter
beta_chi	Hyperparameter
zero_inflation	Logical - was zero inflation modelled?
lambda	Zero inflation parameter

Value

The posterior log-likelihood.

kieranrcampbell/mfa documentation built on March 27, 2022, 5:20 a.m.