The partition function for the Mallows model can be defined in a computationally efficient manner as $$Z_{n}(\alpha) = \sum_{d_{n} \in \mathcal{D}_{n}} N_{m,n} e^{-(\alpha/n) d_{m}}.$$ In this equation, \(\mathcal{D}_{n}\) a set containing all possible distances at the given number of items, and \(d_{m}\) is on element of this set. Finally, \(N_{m,n}\) is the number of possible configurations of the items that give the particular distance. See Irurozki et al. (2016) , Vitelli et al. (2018) , and Crispino et al. (2023) for details.
For footrule distance, the cardinalities come from entry A062869 in the On-Line Encyclopedia of Integer Sequences (OEIS) (Sloane and Inc. 2020) . For Spearman distance, they come from entry A175929, and for Ulam distance from entry A126065.
Usage
get_cardinalities(n_items, metric = c("footrule", "spearman", "ulam"))Value
A dataframe with two columns, distance which contains each distance
in the support set at the current number of items, i.e., \(d_{m}\), and
value which contains the number of values at this particular distances,
i.e., \(N_{m,n}\).
References
Crispino M, Mollica C, Astuti V, Tardella L (2023).
“Efficient and accurate inference for mixtures of Mallows models with Spearman distance.”
Statistics and Computing, 33(5).
ISSN 1573-1375, doi:10.1007/s11222-023-10266-8
, http://dx.doi.org/10.1007/s11222-023-10266-8.
Irurozki E, Calvo B, Lozano JA (2016).
“PerMallows: An R Package for Mallows and Generalized Mallows Models.”
Journal of Statistical Software, 71(12), 1–30.
doi:10.18637/jss.v071.i12
.
Sloane NJA, Inc. TOF (2020).
“The on-line encyclopedia of integer sequences.”
https://oeis.org/.
Vitelli V, Sørensen, Crispino M, Arjas E, Frigessi A (2018).
“Probabilistic Preference Learning with the Mallows Rank Model.”
Journal of Machine Learning Research, 18(1), 1–49.
https://jmlr.org/papers/v18/15-481.html.
See also
Other partition function:
compute_exact_partition_function(),
estimate_partition_function()
Examples
# Extract the cardinalities for four items with footrule distance
n_items <- 4
dat <- get_cardinalities(n_items)
# Compute the partition function at alpha = 2
alpha <- 2
sum(dat$value * exp(-alpha / n_items * dat$distance))
#> [1] 3.572331
#'
# We can confirm that it is correct by enumerating all possible combinations
all <- expand.grid(1:4, 1:4, 1:4, 1:4)
perms <- all[apply(all, 1, function(x) length(unique(x)) == 4), ]
sum(apply(perms, 1, function(x) exp(-alpha / n_items * sum(abs(x - 1:4)))))
#> [1] 3.572331
# We do the same for the Spearman distance
dat <- get_cardinalities(n_items, metric = "spearman")
sum(dat$value * exp(-alpha / n_items * dat$distance))
#> [1] 2.497585
#'
# We can confirm that it is correct by enumerating all possible combinations
sum(apply(perms, 1, function(x) exp(-alpha / n_items * sum((x - 1:4)^2))))
#> [1] 2.497585