BayesMallows: asymptotic_partition_function – R documentation

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asymptotic_partition_function

Asymptotic Approximation of Partition Function

Description

Compute the asymptotic approximation of the logarithm of the partition function, using the iteration algorithm of Mukherjee (2016).

Usage

asymptotic_partition_function(
  alpha_vector,
  n_items,
  metric,
  K,
  n_iterations = 1000L,
  tol = 1e-09
)

Arguments

`alpha_vector`	A numeric vector of alpha values.
`n_items`	Integer specifying the number of items.
`metric`	One of `"footrule"` and `"spearman"`.
`K`	Integer.
`n_iterations`	Integer specifying the number of iterations.
`tol`	Stopping criterion for algorithm. The previous matrix is subtracted from the updated, and if the maximum absolute relative difference is below `tol`, the iteration stops.

Value

A vector, containing the partition function at each value of alpha.

References

Mukherjee S (2016). “Estimation in exponential families on permutations.” The Annals of Statistics, 44(2), 853–875. doi: 10.1214/15-aos1389, https://doi.org/10.1214/15-aos1389.

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

v1.0.1

GPL-3

Authors

Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>), Valeria Vitelli [aut] (<https://orcid.org/0000-0002-6746-0453>), Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut]

Initial release