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 randomly.
smc_mallows_new_item_rank_alpha_fixed(
alpha,
n_items,
R_obs,
metric,
leap_size,
N,
Time,
logz_estimate,
mcmc_kernel_app,
alpha_prop_sd,
lambda,
alpha_max,
aug_method,
verbose = FALSE
)A numeric value of the true scale parameter
Integer is the number of items in a ranking
3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps
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 Integer specifying the step size of the leap-and-shift proposal distribution
Integer specifying the number of particles
Integer specifying the number of time steps in the SMC algorithm
Estimate of the partition function, computed with
estimate_partition_function in the BayesMallow R package estimate_partition_function.
Integer value for the number of applications we apply the MCMC move kernel
Numeric value specifying the standard deviation of the
lognormal proposal distribution used for \(\alpha\) in the
Metropolis-Hastings algorithm. Defaults to 0.1.
Strictly positive numeric value specifying the rate parameter
of the truncated exponential prior distribution of \(\alpha\). Defaults
to 0.1. When n_cluster > 1, each mixture component
\(\alpha_{c}\) has the same prior distribution.
Maximum value of alpha in the truncated exponential
prior distribution.
A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random".
Logical specifying whether to print out the progress of the
SMC-Mallows algorithm. Defaults to FALSE.
a 3d matrix containing the samples of rho and alpha from the SMC algorithm