BayesMallows (version 2.2.3)

set_priors: 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))

Value

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

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 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.

References

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()