expectation.gmm

Compute the expected distance in the GMM under the Hamming distance

Includes functions to work with the Mallows and Generalized Mallows
Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam
and it includes functions for making inference, sampling and learning such
distributions, some of which are novel in the literature. As a by-product,
PerMallows also includes operations for permutations, paying special attention
to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It
is also possible to generate random permutations at a given distance, or with
a given number of inversions, or cycles, or fixed points or even with a given
length on LIS (longest increasing subsequence).

Ekhine Irurozki

PerMallows

Permutations and Mallows Distributions

Borja Calvo

expectation.gmm function

<dl><dt>theta</dt>
<dd>n dimensional real vector with the dispersion parameters</dd>
<dt>dist.name</dt>
<dd>optional name of the distance used in the GMM. One of: kendall (default), cayley, hamming</dd></dl>

Arguments

Compute the expected distance, GMM under the Hamming distance — expectation.gmm

<dl>

<dt>theta</dt>
<dd>n dimensional real vector with the dispersion parameters</dd>


<dt>dist.name</dt>
<dd>optional name of the distance used in the GMM. One of: kendall (default), cayley, hamming</dd>

</dl>

expectation.gmm: Compute the expected distance, GMM under the Hamming distance

Description

Usage

Value

Arguments

References

Examples