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Shuhei Mano

Publications and source records attributed to Shuhei Mano.

At least 19 recordsLinked to original sources

A Class of Gaussian Fields on $\mathbb{Z}_q^d$

Gaussian fields $(g_x)$ on $\mathbb{Z}_q^d$ are constructed from a class of reversible long range random walks $(X_t)_{t\in \mathbb{N}}$ on $\mathbb{Z}_q^d$ in arXiv:2510.22554. The construction is from taking the covariance function of $(g_x)$ as $(1-\alpha)G(x,y;\alpha)$, where $G(x,y;\alpha)$ is the Green function of a random walk with killing in each transition at rate $1-\alpha$. A decomposition of the Gaussian field into a sum of independent Gaussian random variables is made. By letting $q\to \infty$ the Gaussian field becomes defined from an infinite-dimensional random walk on a torus. The random walk model is also extended to $d=\infty$ by considering a de Finetti random walk where entries in the increments of the random walk are exchangeable. A limit Gaussian field on $\mathbb{R}^d$ arises from a central limit theorem approach. The transform of this Gaussian field, which is again a Gaussian field, is calculated. It has a simpler covariance matrix than the original field. The Hamiltonian connected to the Gaussian field is calculated. A limit theorem for the partition function arising from the Hamiltonian is found.

math.PR

Gaussian free fields on Hamming graphs and lattice spin systems

We discuss a class of discrete Gaussian free fields on Hamming graphs, where interactions are determined solely by the Hamming distance between vertices. The purpose of examining this class is that it differs significantly from the commonly discussed spin system on the integer lattice with nearest-neighbour interactions. After introducing general results on the partition function and covariance for the class of Gaussian free fields, we present detailed properties of some specific models. Group-theoretic arguments and the Fourier transform give some explicit results.

math.PR

A Class of Long Range Circulant Random Walks on $\mathbb{Z}_q^d$

This paper studies a class of long range random walks $(X_t)_{t=0}^\infty$ on the direct product of cyclic groups $\mathbb{Z}_q^d$ for $d\ge 1$ and $q\ge 2$. $X_{t+1} = X_t + Z_t \mod q$, with $(Z_t)_{t=1}^\infty$ \iid on $\{0,1,\ldots, q-1\}^d$. Entries of $Z_t$ are updated by circulant matrices, possibly with dependence. Multiple entries of $Z_t$ can be non-zero in a transition. An emphasis is on finding the structure of such random walks and spectral expansions for the transition functions. An extension is made to processes on a $d$-dimensional torus, scaling entries in $\{0,1,\ldots, q-1\}$ by dividing by $q$ and letting $q\to \infty$. The state space is then $d$ circles of unit perimeter, where $0$ and $1$ are identified as the same point in each circle. If the entries of $X_t$ are exchangeable then a grouping of $X_t$ is made by taking counts of the types $0,\ldots, q-1$ in $X_t$. In this grouping the multivariate Krawtchouk polynomials become the eigenvectors. Examples consider cutoff times and mixing times in these processes. A limit form for the multivariate Krawtchouk polynomials is used to find a central limit theorem for the transition distributions in the grouped model as $d \to \infty$.

math.PR

Spectral analysis of hierarchical continuous-time quantum walks

In this paper, we introduce hierarchical random walks at first. In this model, we use two types of random walkers, {global and local} walkers. The global walker chooses a local walker at every step, then the chosen local walker moves a single step. After that we construct the corresponding continuous-time quantum walks and discuss its spectral structures. Then we define multi-dimensional continuous-time quantum walk by taking a marginal distribution respect to the global walker.

quant-ph

Symmetric Quantum Walks on Hamming Graphs and Their Limit Distributions

We study a class of symmetric coined quantum walks on Hamming graphs, where the distance between vertices specifies the transition probability. A special model is the simple quantum walk on the hypercube, which has been discussed in the literature. Eigenvalues of the unitary operator of the quantum walks are zeros of certain self-reciprocal polynomials. We obtain a spectral representation of the wave vector, where our systematic treatment relies on the coin space isomorphic to the state space and the commutative association scheme. The Grover coin is extended to the reflection about a vector in an invariant subspace of the Terwilliger algebra. The limit distributions of several quantum walks are obtained.

quant-ph

Direct sampling from conditional distributions by sequential maximum likelihood estimations

We can directly sample from the conditional distribution of any log-affine model. The algorithm is a Markov chain on a bounded integer lattice, and its transition probability is the ratio of the UMVUE (uniformly minimum variance unbiased estimator) of the expected counts to the total number of counts. The computation of the UMVUE accounts for most of the computational cost, which makes the implementation challenging. Here, we investigated an approximate algorithm that replaces the UMVUE with the MLE (maximum likelihood estimator). Although it is generally not exact, it is efficient and easy to implement; no prior study is required, such as about the connection matrices of the holonomic ideal in the original algorithm.

math.ST

A measure-on-graph-valued diffusion: a particle system with collisions, and their applications

A diffusion taking value in probability measures on a graph with a vertex set $V$, $\sum_{i\in V}x_i\delta_i$, is studied. The masses on each vertices satisfy the stochastic differential equation of the form $dx_i=\sum_{j\in N(i)}\sqrt{x_ix_j}dB_{ij}$ on the simplex, where $\{B_{ij}\}$ are independent standard Brownian motions with skew symmetry and $N(i)$ is the neighbour of the vertex $i$. A dual Markov chain on integer partitions to the Markov semigroup associated with the diffusion is used to show that the support of an extremal stationary state of the adjoint semigroup is an independent set of the graph. We also investigate the diffusion with a linear drift, which gives a killing of the dual Markov chain on a finite integer lattice. The Markov chain is used to study the unique stationary state of the diffusion, which generalizes the Dirichlet distribution. Two applications of the diffusions are discussed: analysis of an algorithm to find an independent set of a graph, and a Bayesian graph selection based on computation of probability of a sample by using coupling from the past.

math.PR

Algorithm for direct sampling from conditional distributions of toric models

We show that contiguity relations of hypergeometric functions of several variables give a direct sampling algorithm from the conditional distribution of toric models in statistics. The algorithm is based on a Markov chain on a lattice generated by a matrix $A$. A correspondence between decomposable graphical models and $A$-hypergeometric systems is discussed. We give a sum formula of special values of $A$-hypergeometric polynomials. Some examples with implementations are presented.

math.ST

Asymptotic Moments Matching to Uniformly Minimum Variance Unbiased Estimation under Ewens Sampling Formula

The Ewens sampling formula is a distribution related to the random partition of a positive integer. In this study, we investigate the issue of non-existence solutions in parameter estimation under the distribution. As a result, the first and second moments matching estimators to the uniformly minimum variance unbiased estimator are derived using the Ewens sampling formula in asymptotic sense. A Monte Carlo simulation study is performed to evaluate the efficiency of the resulting estimators.

math.ST

Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry

A procedure for asymptotic bias reduction of maximum likelihood estimates of generic estimands is developed. The estimator is realized as a plug-in estimator, where the parameter maximizes the penalized likelihood with a penalty function that satisfies a quasi-linear partial differential equation of the first order. The integration of the partial differential equation with the aid of differential geometry is discussed. Applications to generalized linear models, linear mixed-effects models, and a location-scale family are presented.

math.ST

A nonparametric method to assess significance of events in search for gravitational waves with false discovery rate

In this paper, we present a consistent procedure to assess the significance of gravitational wave events observed by laser interferometric gravitational wave detectors based on the background distribution of detection statistic. We propose a non-parametric method to estimate $p$-value. Based on the estimated $p$-values, we propose a new procedure to assess the significance of a particular event with $q$-value which is the minimum false discovery rate that can be attained when calling the event significant. The $q$-value gives us a criterion on the significance of events which is different from $P_{\rm astro}$ which is used in the LIGO-Virgo analysis and in other analysis. The proposed procedure is applied to the 1-OGC and 2-OGC catalogs [2][3]. For most of the events which were claimed significant in [2] and [3], we also obtain the same results. However, there are differences in the significance for several marginal events. Since the proposed procedure does not require any assumptions on signal and noise, it is very simple and straightforward. The procedure is also applicable to other searches for gravitational waves whose background distribution of detection statistic is difficult to know.

gr-qc

A reversal phenomenon in estimation based on multiple samples from the Poisson--Dirichlet distribution

Consider two forms of sampling from a population: (i) drawing $s$ samples of $n$ elements with replacement and (ii) drawing a single sample of $ns$ elements. In this paper, under the setting where the descending order population frequency follows the Poisson--Dirichlet distribution with parameter $θ$, we report that the magnitude relation of the Fisher information, which sample partitions converted from samples (i) and (ii) possess, can change depending on the parameters, $n$, $s$, and $θ$. Roughly speaking, if $θ$ is small relative to $n$ and $s$, the Fisher information of (i) is larger than that of (ii); on the contrary, if $θ$ is large relative to $n$ and $s$, the Fisher information of (ii) is larger than that of (i). The result represents one aspect of random distributions.

math.ST

Partition structure and the A-hypergeometric distribution associated with the rational normal curve

A distribution whose normalization constant is an A-hypergeometric polynomial is called an A-hypergeometric distribution. Such a distribution is in turn a generalization of the generalized hypergeometric distribution on the contingency tables with fixed marginal sums. In this paper, we will see that an A-hypergeometric distribution with a homogeneous matrix of two rows, especially, that associated with the rational normal curve, appears in inferences involving exchangeable partition structures. An exact sampling algorithm is presented for the general (any number of rows) A-hypergeometric distributions. Then, the maximum likelihood estimation of the A-hypergeometric distribution associated with the rational normal curve, which is an algebraic exponential family, is discussed. The information geometry of the Newton polytope is useful for analyzing the full and the curved exponential family. Algebraic methods are provided for evaluating the A-hypergeometric polynomials.

math.ST

The star-shaped Lambda-coalescent and Fleming-Viot process

The star-shaped $Λ$-coalescent and corresponding $Λ$-Fleming-Viot process where the $Λ$ measure has a single atom at unity are studied in this paper. The transition functions and stationary distribution of the $Λ$-Fleming-Viot process are derived in a two-type model with mutation. The distribution of the number of non-mutant lines back in time in the star-shaped $Λ$-coalescent is found. Extensions are made to a model with $d$ types, either with parent independent mutation or general Markov mutation, and an infinitely-many-types model when $d\to \infty$. An eigenfunction expansion for the transition functions is found which has polynomial right eigenfunctions and left eigenfunctions described by hyperfunctions. A further star-shaped model with general frequency dependent change is considered and the stationary distribution in the Fleming-Viot process derived. This model includes a star-shaped $Λ$-Fleming-Viot process with mutation and selection. In a general $Λ$-coalescent explicit formulae for the transition functions and stationary distribution when there is mutation are unknown, however in this paper explicit formulae are derived in the star-shaped coalescent.

math.PR

Extreme sizes in the Gibbs-type exchangeable random partitions

Gibbs-type exchangeable random partitions, which is a class of multiplicative measures on the set of positive integer partitions, appear in various contexts, including Bayesian statistics, random combinatorial structures, and stochastic models of diversity in various phenomena. Some distributional results on ordered sizes in the Gibbs partition are established by introducing associated partial Bell polynomials and analysis of the generating functions. The combinatorial approach is applied to derive explicit results on asymptotic behavior of the extreme sizes in the Gibbs partition. Especially, Ewens-Pitman partition, which is the sample from the Poisson-Dirichlet process and has been discussed from rather model-specific viewpoints, and a random partition which was recently introduced by Gnedin, are discussed in the details. As by-products, some formulas for the associated partial Bell polynomials are presented.

math.ST

Kernel Approximate Bayesian Computation for Population Genetic Inferences

Approximate Bayesian computation (ABC) is a likelihood-free approach for Bayesian inferences based on a rejection algorithm method that applies a tolerance of dissimilarity between summary statistics from observed and simulated data. Although several improvements to the algorithm have been proposed, none of these improvements avoid the following two sources of approximation: 1) lack of sufficient statistics: sampling is not from the true posterior density given data but from an approximate posterior density given summary statistics; and 2) non-zero tolerance: sampling from the posterior density given summary statistics is achieved only in the limit of zero tolerance. The first source of approximation can be improved by adding a summary statistic, but an increase in the number of summary statistics could introduce additional variance caused by the low acceptance rate. Consequently, many researchers have attempted to develop techniques to choose informative summary statistics. The present study evaluated the utility of a kernel-based ABC method (Fukumizu et al. 2010, arXiv:1009.5736 and 2011, NIPS 24: 1549-1557) for complex problems that demand many summary statistics. Specifically, kernel ABC was applied to population genetic inference. We demonstrate that, in contrast to conventional ABCs, kernel ABC can incorporate a large number of summary statistics while maintaining high performance of the inference.

q-bio.PE

Duality between the two-locus Wright-Fisher Diffusion Model and the Ancestral Process with Recombination

Known results on the moments of the distribution generated by the two-locus Wright-Fisher diffusion model and a duality between the diffusion process and the ancestral process with recombination are briefly summarized. A numerical methods for computing moments by a Markov chain Monte Carlo and a method to compute closed-form expressions of the moments are presented. By using the duality argument properties of the ancestral recombination graph are studied in terms of the moments.

q-bio.PE

Ancestral Graph with Bias in Gene Conversion

Gene conversion is a mechanism by which a double-strand break in a DNA molecule is repaired using a homologous DNA molecule as a template. As a result, one gene is 'copied and pasted' onto the other gene. It was recently reported that the direction of gene conversion appears to be biased towards G and C nucleotides. In this paper a stochastic model of the dynamics of the bias in gene conversion is developed for a finite population of members in a multigene family. The dual process is the biased voter model, which generates an ancestral random graph for a given sample. An importance-sampling algorithm for computing the likelihood of the sample is also given.

q-bio.PE