conjugacy: Draw Conjugate Samples

In eheinzen/gibbs.utils: Utilities for Gibbs Sampling

Draw Conjugate Samples

Description

Sample from the posterior (conditional on all other parameters) in the conjugate setting.

Usage

conj_norm_mu(y, tau, mu0 = 0, tau0 = 0.001, ..., mult = 1, params.only = FALSE)

conj_mvnorm_mu(
  y,
  Q,
  mu0 = NULL,
  Q0 = diag(0.001, p),
  ...,
  newQ = mult * Q0 + n * Q,
  newQ.chol = gu_chol(newQ),
  mult = 1,
  params.only = FALSE
)

conj_matnorm_mu(
  y,
  V,
  U = NULL,
  mu0 = NULL,
  Q0,
  ...,
  newQ = V %x% U + Q0,
  newQ.chol = gu_chol(newQ),
  diag = FALSE,
  zero = NULL,
  params.only = FALSE
)

conj_lm_beta(
  y,
  X,
  XtX = t(X) %*% X,
  tau,
  mu0 = NULL,
  Q0,
  ...,
  newQ = tau * XtX + Q0,
  newQ.chol = gu_chol(newQ),
  params.only = FALSE
)

conj_matlm_beta(
  y,
  X,
  V,
  U = NULL,
  mu0 = NULL,
  Q0,
  ...,
  XtU = if (is.null(U)) t(X) else t(X) %*% U,
  XtUX = XtU %*% X,
  newQ = V %x% XtUX + Q0,
  newQ.chol = gu_chol(newQ),
  diag = FALSE,
  zero = NULL,
  params.only = FALSE
)

conj_norm_tau(y, mu, a0 = 0.001, b0 = 0.001, params.only = FALSE)

conj_mvnorm_Q(y, mu = NULL, V0, v0, V0_inv = chol_inv(V0), params.only = FALSE)

conj_matnorm_V(
  y,
  mu = NULL,
  U = NULL,
  V0,
  v0,
  ...,
  ytUy = t(ymu) %*% U %*% ymu,
  V0_inv = chol_inv(V0),
  params.only = FALSE
)

conj_lm_tau(
  y,
  X,
  beta,
  Xbeta = X %*% beta,
  a0 = 0.001,
  b0 = 0.001,
  params.only = FALSE
)

conj_binom_p(k, n, a0 = 1, b0 = 1, params.only = FALSE)

conj_gamma_b(x, a, a0, b0, params.only = FALSE)

Arguments

y, x	realizations from the distribution whose parameter is being drawn. For multivariate conjugacy, this is an n-by-p matrix
mu0	the prior mean of mu or beta. The default (NULL) is the same as an appropriately-dimensioned vector/matrix of all zeros, but a little faster.
...	Other arguments. Examples include verbose=, take.chol=, and Rstruct=.
mult	An optional multiplier for the prior precision; useful in some cases
params.only	Should just a list of the updated parameters be returned?
Q, tau	the precision of the (multivariate) normal distribution from which y comes
Q0, tau0	the prior precision of mu.
V, U	the precision matrices for the matrix-normal distribution
diag	If TRUE, both V and Q0 are assumed (but not confirmed!) to be diagonal, which can speed up the Cholesky decomposition up to 100x. Default FALSE.
zero	A matrix of ones and zeros, the same size as the beta to sample. Zero indicates a structural zero in the beta. In the event that this is specified, everything returned is of size sum(zero).
X	the data matrix on beta
XtX, XtU, XtUX, ytUy, V0_inv, Xbeta, newQ, newQ.chol	pre-computed "shortcut" arguments for efficiency reasons
mu	the mean of the normal distribution from which y comes
a0, b0	the parameters (shape and rate) of the gamma distribution prior on tau, or the parameters (shape1 and shape2) of the beta distribution prior on p.
V0, v0	the parameters (matrix and degrees of freedom) of the Wishart prior on Q or V
beta	the coefficients on X
k	The number of successes
n	The number of trials
a	The shape parameter for the gamma distribution

eheinzen/gibbs.utils documentation built on Sept. 27, 2024, 9:03 p.m.