smc_mallows_new_item_rank: SMC-Mallows new item rank

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

SMC-Mallows new item rank

Description

Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudolikelihood augmentation.

Usage

smc_mallows_new_item_rank(
  n_items,
  R_obs,
  N,
  Time,
  logz_estimate,
  cardinalities,
  mcmc_kernel_app,
  aug_rankings_init = NULL,
  rho_samples_init = NULL,
  alpha_samples_init = 0L,
  alpha = 0,
  alpha_prop_sd = 0.5,
  lambda = 0.1,
  alpha_max = 1e+06,
  aug_method = "random",
  verbose = FALSE,
  alpha_fixed = FALSE,
  metric = "footrule",
  leap_size = 1L
)

Arguments

n_items	Integer is the number of items in a ranking
R_obs	3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps
N	Integer specifying the number of particles
Time	Integer specifying the number of time steps in the SMC algorithm
logz_estimate	Estimate of the partition function, computed with estimate_partition_function.
cardinalities	Cardinalities for exact computation of partition function, returned from prepare_partition_function.
mcmc_kernel_app	Integer value for the number of applications we apply the MCMC move kernel
aug_rankings_init	Initial values for augmented rankings.
rho_samples_init	Initial values for rho samples.
alpha_samples_init	Initial values for alpha samples.
alpha	numeric value of the scale parameter.
alpha_prop_sd	Numeric value of the standard deviation of the prior distribution for alpha
lambda	Strictly positive numeric value specifying the rate parameter of the truncated exponential prior distribution of alpha.
alpha_max	Maximum value of alpha in the truncated exponential prior distribution.
aug_method	A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"
verbose	Logical specifying whether to print out the progress of the SMC-Mallows algorithm. Defaults to FALSE.
alpha_fixed	Logical indicating whether to sample alpha or not.
metric	A character string specifying the distance metric to use in the Bayesian Mallows Model. Available options are "footrule", "spearman", "cayley", "hamming", "kendall", and "ulam".
leap_size	leap_size Integer specifying the step size of the leap-and-shift proposal distribution

Value

a 3d matrix containing: the samples of: rho, alpha and the augmented rankings, and the effective sample size at each iteration of the SMC algorithm.

See Also

Other modeling: compute_mallows_mixtures(), compute_mallows(), smc_mallows_new_users()

BayesMallows documentation built on Nov. 25, 2023, 5:09 p.m.