Set prior parameters for Bayesian Mallows model

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))

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 vitelli2018BayesMallows.
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 crispino2019BayesMallows for details.

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