Set values related to the prior distributions for the Bayesian Mallows model.
set_priors(gamma = 1, lambda = 0.001, psi = 10, kappa = c(1, 3))An object of class "BayesMallowsPriors", to be provided in the
priors argument to compute_mallows(), compute_mallows_mixtures(), or
update_mallows().
Strictly positive numeric value specifying the shape parameter
of the gamma prior distribution of \(\alpha\). Defaults to 1, thus
recovering the exponential prior distribution used by
vitelli2018BayesMallows.
Strictly positive numeric value specifying the rate parameter
of the gamma prior distribution of \(\alpha\). Defaults
to 0.001. When n_cluster > 1, each mixture component \(\alpha_{c}\)
has the same prior distribution.
Positive integer specifying the concentration parameter \(\psi\)
of the Dirichlet prior distribution used for the cluster probabilities
\(\tau_{1}, \tau_{2}, \dots, \tau_{C}\), where \(C\) is the value of
n_clusters. Defaults to 10L. When n_clusters = 1, this argument is
not used.
Hyperparameters of the truncated Beta prior used for error
probability \(\theta\) in the Bernoulli error model. The prior has the
form \(\pi(\theta) = \theta^{\kappa_{1}} (1 - \theta)^{\kappa_{2}}\).
Defaults to c(1, 3), which means that the \(\theta\) is a priori
expected to be closer to zero than to 0.5. See
crispino2019BayesMallows for details.
Other preprocessing:
get_transitive_closure(),
set_compute_options(),
set_initial_values(),
set_model_options(),
set_progress_report(),
set_smc_options(),
setup_rank_data()