set_priors: Set prior parameters for Bayesian Mallows model

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Set prior parameters for Bayesian Mallows model

Description

Set values related to the prior distributions for the Bayesian Mallows model.

Usage

set_priors(gamma = 1, lambda = 0.001, psi = 10, kappa = c(1, 3))

Arguments

gamma	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 \insertCitevitelli2018BayesMallows.
lambda	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.
psi	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.
kappa	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 \insertCitecrispino2019BayesMallows for details.

Value

An object of class "BayesMallowsPriors", to be provided in the priors argument to compute_mallows(), compute_mallows_mixtures(), or update_mallows().

References

\insertAllCited

See Also

Other preprocessing: get_transitive_closure(), set_compute_options(), set_initial_values(), set_model_options(), set_progress_report(), set_smc_options(), setup_rank_data()

BayesMallows documentation built on June 13, 2025, 9:08 a.m.