arXiv subjects

Han L. Gan

Publications and source records attributed to Han L. Gan.

5 recordsLinked to original sources

arXiv2025

Poisson-Dirichlet approximation for the stationary distribution of the inclusion process

We consider the approximation of the stationary distribution of the finite inclusion process with the Poisson-Dirichlet distribution. Using Stein's method, we derive an explicit bound for the approximation error, which is of order 1/N in the thermodynamic limit. The results are achieved from a minor modification to Stein's method for Poisson-Dirichlet distribution approximation developed in Gan & Ross (2021). The derivatives used on test functions in Gan & Ross (2021) were directional type derivatives specifically chosen for their measure preserving properties. Depending upon the application, these derivatives can prove cumbersome. In this note, we show that for certain test functions we can instead use more traditional derivatives, which simplifies the bounds for the Stein factors and is more amenable to the approximation of the inclusion process.

math.PR↗

arXiv2024

Stationary distribution approximations of Two-island Wright-Fisher and seed-bank models using Stein's method

We consider two finite population Markov chain models, the two-island Wright-Fisher model with mutation, and the seed-bank model with mutation. Despite the relatively simple descriptions of the two processes, the the exact form of their stationary distributions is in general intractable. For each of the two models we provide two approximation theorems with explicit upper bounds on the distance between the stationary distributions of the finite population Markov chains, and either the stationary distribution of a two-island diffusion model, or the beta distribution. We show that the order of the bounds, and correspondingly the appropriate choice of approximation, depends upon the relative sizes of mutation and migration. In the case where migration and mutation are of the same order, the suitable approximation is the two-island diffusion model, and if migration dominates mutation, then the weighted average of both islands is well approximated by a beta random variable. Our results are derived from a new development of Stein's method for the stationary distribution of the two-island diffusion model for the weak migration results, and utilising the existing framework for Stein's method for the Dirichlet distribution.

math.PR↗

arXiv2023

Steady-state Dirichlet approximation of the Wright-Fisher model using the prelimit generator comparison approach of Stein's method

The Wright-Fisher model, originating in Wright (1931) is one of the canonical probabilistic models used in mathematical population genetics to study how genetic type frequencies evolve in time. In this paper we bound the rate of convergence of the stationary distribution for a finite population Wright-Fisher Markov chain with parent independent mutation to the Dirichlet distribution. Our result improves the rate of convergence established in Gan et al. (2017) from $O(1/\sqrt{N})$ to $O(1/N)$. The results are derived using Stein's method, in particular, the prelimit generator comparison method.

math.PR↗

arXiv2020

Stein's method for the Poisson-Dirichlet distribution and the Ewens Sampling Formula, with applications to Wright-Fisher models

We provide a general theorem bounding the error in the approximation of a random measure of interest--for example, the empirical population measure of types in a Wright-Fisher model--and a Dirichlet process, which is a measure having Poisson-Dirichlet distributed atoms with i.i.d. labels from a diffuse distribution. The implicit metric of the approximation theorem captures the sizes and locations of the masses, and so also yields bounds on the approximation between the masses of the measure of interest and the Poisson-Dirichlet distribution. We apply the result to bound the error in the approximation of the stationary distribution of types in the finite Wright-Fisher model with infinite-alleles mutation structure (not necessarily parent independent) by the Poisson-Dirichlet distribution. An important consequence of our result is an explicit upper bound on the total variation distance between the random partition generated by sampling from a finite Wright-Fisher stationary distribution, and the Ewens Sampling Formula. The bound is small if the sample size $n$ is much smaller than $N^{1/6}\log(N)^{-1/2}$, where $N$ is the total population size. Our analysis requires a result of separate interest, giving an explicit bound on the second moment of the number of types of a finite Wright-Fisher stationary distribution. The general approximation result follows from a new development of Stein's method for the Dirichlet process, which follows by viewing the Dirichlet process as the stationary distribution of a Fleming-Viot process, and then applying Barbour's generator approach.

math.PR↗

arXiv2019

Stein's method and duality of Markov processes

One of the key ingredients to successfully apply Stein's method for distributional approximation are solutions to the Stein equations and their derivatives. Using Barbour's generator approach, one can solve for the solutions to the Stein equation in terms of the semi-group of a Markov process, which is typically a diffusion process if it is a continuous distribution. For an arbitrary diffusion it can a difficult task to evaluate the semi-group and its derivatives. In this paper, for polynomial test functions, instead of calculating the semi-group of a diffusion, via a duality argument, we instead utilise the semi-group of a much simpler Markov jump process. This approach yields a new method for explicitly solving for the solutions of Stein equations for diffusion processes. We present both the general idea of the approach and examples for both univariate and multivariate distributions.

math.PR↗