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We explain how to construct matrix product stationary states which are composed of finite-dimensional matrices. Our construction explained in this article was first presented in a part of [Hieida and Sasamoto:J. Phys. A: Math. Gen. 37…

Statistical Mechanics · Physics 2016-11-23 Yasuhiro Hieida , Tomohiro Sasamoto

Using the method of equivariant moving frames, we present a procedure for constructing symmetry-preserving finite element methods for second-order ordinary differential equations. Using the method of lines, we then indicate how our…

Numerical Analysis · Mathematics 2018-03-28 Alexander Bihlo , Francis Valiquette

We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris (1972). Since the EB algorithm is essentially…

Machine Learning · Statistics 2019-04-10 Takeru Matsuda , Fumiyasu Komaki

A novel data-driven methodology is presented for the joint selection of prior parameters for both fixed and random effects in Linear Mixed Models (LMMs). This approach facilitates the estimation of complex random-effects structures, as well…

Methodology · Statistics 2026-04-28 Matteo Amestoy , R. Vermeulen , Mark A. van de Wiel , Wessel N. van Wieringen

The exact stationary state of an asymmetric exclusion process with fully parallel dynamics is obtained using the matrix product Ansatz. We give a simple derivation for the deterministic case by a physical interpretation of the dimension of…

Statistical Mechanics · Physics 2011-11-29 Jan de Gier , Bernard Nienhuis

Conditions for positive and polynomial recurrence have been proposed for a class of reliability models of two elements with transitions from working state to failure and back. As a consequence, uniqueness of stationary distribution of the…

Probability · Mathematics 2020-05-29 Alexander Veretennikov

Energy-based models (EBMs) implement inference as gradient descent on a learned Lyapunov function, yielding interpretable, structure-preserving alternatives to black-box neural ODEs and aligning naturally with physical AI. Yet their use in…

Systems and Control · Electrical Eng. & Systems 2026-04-03 Simone Betteti , Luca Laurenti

In this paper we extend the polynomial time integration framework to include exponential integration for both partitioned and unpartitioned initial value problems. We then demonstrate the utility of the exponential polynomial framework by…

Numerical Analysis · Mathematics 2021-02-05 Tommaso Buvoli

Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality of context extension. In this work, we proposeExtensible…

Computation and Language · Computer Science 2024-02-20 Kun Luo , Zheng Liu , Shitao Xiao , Kang Liu

We consider a stochastic matching model with a general compatibility graph, as introduced in \cite{MaiMoy16}. We show that the natural necessary condition of stability of the system is also sufficient for the natural matching policy 'First…

Probability · Mathematics 2023-06-22 Pascal Moyal , Ana Busic , Jean Mairesse

Modeling relations between individuals is a classical question in social sciences, ecology, etc. In order to uncover a latent structure in the data, a popular approach consists in clustering individuals according to the observed patterns of…

Methodology · Statistics 2020-02-28 Avner Bar-Hen , Pierre Barbillon , Sophie Donnet

Since the 1970s there has been a rich theory of equilibrium states over shift spaces associated to H\"older-continuous real-valued potentials. The construction of equilibrium states associated to matrix-valued potentials is much more…

Dynamical Systems · Mathematics 2016-12-07 Ian D. Morris

In this work the issue of Bayesian inference for stationary data is addressed. Therefor a parametrization of a statistically suitable subspace of the the shift-ergodic probability measures on a Cartesian product of some finite state space…

Statistics Theory · Mathematics 2017-10-24 Fritz Moritz von Rohrscheidt

The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a…

Methodology · Statistics 2025-03-19 Emre Anakok , Pierre Barbillon , Colin Fontaine , Elisa Thebault

This paper presents new theory and methodology for the Bayesian estimation of overfitted hidden Markov models, with finite state space. The goal is then to achieve posterior emptying of extra states. A prior configuration is constructed…

Methodology · Statistics 2016-02-09 Zoé van Havre , Judith Rousseau , Nicole White , Kerrie Mengersen

We propose to learn energy-based model (EBM) in the latent space of a generator model, so that the EBM serves as a prior model that stands on the top-down network of the generator model. Both the latent space EBM and the top-down network…

Machine Learning · Statistics 2020-10-30 Bo Pang , Tian Han , Erik Nijkamp , Song-Chun Zhu , Ying Nian Wu

We perform a qualitative analysis of the critical equation associated with a stationary ergodic Hamiltonian through a stochastic version of the metric method, where the notion of closed random stationary set, issued from stochastic…

Analysis of PDEs · Mathematics 2016-02-10 Andrea Davini , Antonio Siconolfi

Following up on purely theoretical work of Bredereck et al. [AAAI 2020], we contribute further theoretical insights into adapting stable two-sided matchings to change. Moreover, we perform extensive empirical studies hinting at numerous…

Computer Science and Game Theory · Computer Science 2021-12-14 Niclas Boehmer , Klaus Heeger , Rolf Niedermeier

In the present work, we demonstrate how the pseudoinverse concept from linear algebra can be used to represent and analyze the boundary conditions of linear systems of partial differential equations. This approach has theoretical and…

Numerical Analysis · Mathematics 2024-01-05 Pelle Olsson

The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables). The requirement is not met when parameters…

Signal Processing · Electrical Eng. & Systems 2024-04-18 Geethu Joseph