get_MVG_likelihood: Get likelihood

In hlplab/MVBeliefUpdatr: Fitting, Summarizing, and Visualizing of Gaussian Ideal Observers and Adaptors

Get likelihood

Description

Get likelihood of observation(s) x given the MVG parameters mu and Sigma. This is the density of a multivariate normal distribution over k dimensions.

Usage

get_MVG_likelihood(
  x,
  mu,
  Sigma,
  Sigma_noise = NULL,
  noise_treatment = infer_default_noise_treatment(Sigma_noise),
  log = T
)

get_likelihood_from_MVG(
  x,
  model,
  noise_treatment = infer_default_noise_treatment(model$Sigma_noise),
  log = T,
  category = "category",
  category.label = NULL,
  wide = FALSE
)

Arguments

x	Observations. Can be a vector with k elements for a single observation or a matrix with k columns and n rows, in which case each row of the matrix is taken to be one observation. If x is a tibble with k columns or a list of vectors of length k, it is reduced into a matrix with k columns.
mu	The category mean mu. Should be a k x 1 or 1 x k matrix, or vector of length k.
Sigma	The category covariance matrix Sigma. Should be a square k x k matrix.
Sigma_noise	Optionally, a covariance matrix describing the perceptual noise to be applied while calculating the posterior predictive. (default: 'NULL')
noise_treatment	Determines whether perceptual noise is considered during categorization, and how. Can be "no_noise", "sample", or "marginalize". If "no_noise", no noise will be applied to the input, and no noise will be assumed during categorization. If "marginalize", average noise (i.e., no noise) will be added to the stimulus, and 'Sigma_noise' is added to Sigma when calculating the likelihood. This simulates the expected consequences for perceptual noise on categorization *in the limit*, i.e, if the input was categorized infinitely many times. If "sample", then noise is sampled and applied to the input, and 'Sigma_noise' is added to Sigma when calculating the likelihood. This simulates the consequence of perceptual noise *on a particular observation*. If "sample" or "marginalize" are chosen, 'Sigma_noise' must be a covariance matrix of appropriate dimensions. (default: "no_noise" if Sigma_noise is NULL, "marginalize" otherwise).
log	Should the log-transformed density be returned ('TRUE')? (default: 'TRUE')

See Also

TBD

hlplab/MVBeliefUpdatr documentation built on July 5, 2025, 6:42 a.m.