@article{23155,
  abstract     = {In this work we establish several monotonicity and decomposition results in the framework of random regular graphs. Among other results, we
show that, for a wide range of parameters d1 ≤ d2, there exists a coupling of
G(n,d1) and G(n,d2) satisfying that G(n,d1) ⊆ G(n,d2) with high probability, confirming a conjecture of Gao, Isaev and McKay in a new regime.
Our contributions include new tools for analysing contiguity and total variation distance between random regular graph models, a novel procedure for
generating unions of random edge-disjoint perfect matchings, and refined estimates of Gao’s bounds on the number of perfect matchings in random regular graphs. In addition, we make progress towards another conjecture of Isaev,
McKay, Southwell and Zhukovskii.},
  author       = {Hollom, Lawrence and Lichev, Lyuben and Mond, Adva and Portier, Julien and Wang, Yiting},
  issn         = {2168-8737},
  journal      = {Annals of Applied Probability},
  keywords     = {Random regular graphs, coupling, contiguity, total variation distance, decomposition},
  number       = {5},
  pages        = {4716--4738},
  publisher    = {Institute of Mathematical Statistics},
  title        = {{Monotonicity and decompositions of random regular graphs}},
  doi          = {10.1214/26-AAP2345},
  volume       = {36},
  year         = {2026},
}

@article{4253,
  abstract     = {We consider a single genetic locus which carries two alleles, labelled P and Q. This locus experiences selection and mutation. It is linked to a second neutral locus with recombination rate r. If r = 0, this reduces to the study of a single selected locus. Assuming a Moran model for the population dynamics, we pass to a diffusion approximation and, assuming that the allele frequencies at the selected locus have reached stationarity, establish the joint generating function for the genealogy of a sample from the population and the frequency of the P allele. In essence this is the joint generating function for a coalescent and the random background in which it evolves. We use this to characterize, for the diffusion approximation, the probability of identity in state at the neutral locus of a sample of two individuals (whose type at the selected locus is known) as solutions to a system of ordinary differential equations. The only subtlety is to find the boundary conditions for this system. Finally, numerical examples are presented that illustrate the accuracy and predictions of the diffusion approximation. In particular, a comparison is made between this approach and one in which the frequencies at the selected locus are estimated by their value in the absence of fluctuations and a classical structured coalescent model is used.},
  author       = {Barton, Nicholas H and Etheridge, Alison and Sturm, Anja},
  issn         = {2168-8737},
  journal      = {Annals of Applied Probability},
  number       = {2},
  pages        = {754 -- 785},
  publisher    = {Institute of Mathematical Statistics},
  title        = {{Coalescence in a Random Background}},
  doi          = {10.1214/105051604000000099},
  volume       = {14},
  year         = {2004},
}