sample_prior: Sample from prior distribution

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Sample from prior distribution

Description

Function to obtain samples from the prior distributions of the Bayesian Mallows model. Intended to be given to update_mallows().

Usage

sample_prior(n, n_items, priors = set_priors())

Arguments

n	An integer specifying the number of samples to take.
n_items	An integer specifying the number of items to be ranked.
priors	An object of class "BayesMallowsPriors" returned from set_priors().

Value

An object of class "BayesMallowsPriorSample", containing n independent samples of \alpha and \rho.

See Also

Other modeling: burnin(), burnin<-(), compute_mallows(), compute_mallows_mixtures(), compute_mallows_sequentially(), update_mallows()

Examples

# We can use a collection of particles from the prior distribution as
# initial values for the sequential Monte Carlo algorithm.
# Here we start by drawing 1000 particles from the priors, using default
# parameters.
prior_samples <- sample_prior(1000, ncol(sushi_rankings))
# Next, we provide the prior samples to update_mallws(), together
# with the first five rows of the sushi dataset
model1 <- update_mallows(
  model = prior_samples,
  new_data = setup_rank_data(sushi_rankings[1:5, ]))
plot(model1)

# We keep adding more data
model2 <- update_mallows(
  model = model1,
  new_data = setup_rank_data(sushi_rankings[6:10, ]))
plot(model2)

model3 <- update_mallows(
  model = model2,
  new_data = setup_rank_data(sushi_rankings[11:15, ]))
plot(model3)

BayesMallows documentation built on Sept. 11, 2024, 5:31 p.m.