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Markov chains provide a foundational framework for modeling sequential stochastic processes, with the transition probability matrix characterizing the dynamics of state evolution. While classical estimation methods such as maximum…

Methodology · Statistics 2025-07-11 Agamani Saha , Souvik Roy

It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack --- and learnability by a noise-free process that mixes…

Machine Learning · Computer Science 2017-03-09 Richard Nock , Giorgio Patrini , Finnian Lattimore , Tiberio Caetano

We present two data-driven procedures to estimate the transition density of an homogeneous Markov chain. The first yields to a piecewise constant estimator on a suitable random partition. By using an Hellinger-type loss, we establish…

Statistics Theory · Mathematics 2012-10-19 Mathieu Sart

We construct admissible circulant Laplacian matrix functions as generators for strictly increasing random walks on the integer line. These Laplacian matrix functions refer to a certain class of Bernstein functions. The approach has…

Probability · Mathematics 2020-12-10 Thomas M. Michelitsch , Federico Polito , Alejandro P. Riascos

We consider random graphs $\mathcal{G}$ built on a homogeneous Poisson point process on $\mathbb{R}^d$, $d\geq 2$, with points $x$ marked by i.i.d. random variables $E_x$. Fixed a symmetric function $h(\cdot, \cdot)$, the vertexes of…

Probability · Mathematics 2021-02-25 Alessandra Faggionato , Hlafo Alfie Mimun

We demonstrate that the probability distribution of the net number of electrons passing through a quantum system in a junction obeys a steady-state fluctuation theorem (FT) which can be tested experimentally by the full counting statistics…

Mesoscale and Nanoscale Physics · Physics 2015-06-25 Massimiliano Esposito , Upendra Harbola , Shaul Mukamel

Divide-and-conquer Bayesian methods consist of three steps: dividing the data into smaller computationally manageable subsets, running a sampling algorithm in parallel on all the subsets, and combining parameter draws from all the subsets.…

Methodology · Statistics 2021-06-01 Chunlei Wang , Sanvesh Srivastava

We study the shifted analogue of the "Lie--Poisson" construction for $L_\infty$ algebroids and we prove that any $L_\infty$ algebroid naturally gives rise to shifted derived Poisson manifolds. We also investigate derived Poisson structures…

Quantum Algebra · Mathematics 2021-03-10 Ruggero Bandiera , Zhuo Chen , Mathieu Stiénon , Ping Xu

Representations of branching Markov processes and their measure-valued limits in terms of countable systems of particles are constructed for models with spatially varying birth and death rates. Each particle has a location and a "level,"…

Probability · Mathematics 2011-04-11 Thomas G. Kurtz , Eliane R. Rodrigues

We study the behavior of the random walk in a continuum independent long-range percolation model, in which two given vertices $x$ and $y$ are connected with probability that asymptotically behaves like $|x-y|^{-\alpha}$ with $\alpha>d$,…

Probability · Mathematics 2022-09-30 Ercan Sönmez , Arnaud Rousselle

We consider the P\'olya random walk in $\mathbb{Z}^2$. The paper establishes a number of results for the distributions and expectations of the number of usual (undirected) and specifically defined in the paper up- and down-directed…

Probability · Mathematics 2023-03-30 Vyacheslav M. Abramov

We introduce an analytical $XX$ spin chain with asymmetrical transport properties. It has an even number $N+1$ of sites labeled by $n=0,\cdots N$. It does not exhibit perfect state transfer (PST) from end-to-end but rather from the first…

Quantum Physics · Physics 2020-01-08 Gabriel Coutinho , Luc Vinet , Hanmeng Zhan , Alexei Zhedanov

Suppose $C$ is a subset of non-zero vectors from the vector space $\mathbb{Z}_2^d$. The cubelike graph $X(C)$ has $\mathbb{Z}_2^d$ as its vertex set, and two elements of $\mathbb{Z}_2^d$ are adjacent if their difference is in $C$. If $M$ is…

Combinatorics · Mathematics 2011-06-30 Wang-Chi Cheung , Chris Godsil

We study how quantum states are scrambled via braiding in systems of non-Abelian anyons through the lens of entanglement spectrum statistics. In particular, we focus on the degree of scrambling, defined as the randomness produced by…

Strongly Correlated Electrons · Physics 2019-01-21 Zhi-Cheng Yang , Konstantinos Meichanetzidis , Stefanos Kourtis , Claudio Chamon

We consider a version of continuum long-range percolation on finite boxes of $\mathbb{R}^d$ in which the vertex set is given by the points of a Poisson point process and each pair of two vertices at distance $r$ is connected with…

Probability · Mathematics 2023-11-21 Ercan Sönmez

Scattering on a resonance state coupled to a complicated background is a typical problem for mesoscopic quantum many-body systems as well as for wave propagation in the presence of a complex environment. On average, such a simple mode…

Mesoscale and Nanoscale Physics · Physics 2020-03-09 Dmitry V. Savin

Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination…

Artificial Intelligence · Computer Science 2020-12-17 Rebekka Burkholz , John Quackenbush

Perfect state transfer between qubits on a uniformly coupled network, with interactions specified by a graph, has advantages over an engineered chain, such as much faster transfer times (independent of the distance between the input and…

Quantum Physics · Physics 2018-11-27 Alastair Kay

The bike sharing systems are fast increasing as a public transport mode in urban short trips, and have been developed in many major cities around the world. A major challenge in the study of bike sharing systems is that large-scale and…

Probability · Mathematics 2016-12-05 Quan-Lin Li , Chang Chen , Rui-Na Fan , Liang Xu , Jing-Yu Ma

We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number of states has to be pre-specified, and Bayesian inference…

Methodology · Statistics 2021-08-12 Yu Luo , David A. Stephens
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