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)
