R: Set prior parameters for Bayesian Mallows model

set_priors {BayesMallows}

R Documentation

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 (Vitelli et al. 2018).
`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 (Crispino et al. 2019) 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