English
Related papers

Related papers: Pairwise Comparisons with Flexible Time-Dynamics

200 papers

We propose a probabilistic semantic filtering framework in which parameters of a dynamical system are inferred and associated with a closed set of semantic classes in a map. We extend existing methods to a multi-parameter setting using a…

Systems and Control · Electrical Eng. & Systems 2026-01-15 Marcus Greiff , Ray Zhang , Thomas Lew , John Subosits

Bayesian probabilistic numerical methods are a set of tools providing posterior distributions on the output of numerical methods. The use of these methods is usually motivated by the fact that they can represent our uncertainty due to…

Computation · Statistics 2018-08-01 Xiaoyue Xi , François-Xavier Briol , Mark Girolami

This paper is dealing with another multiple person game model under the antagonistic duel type setup. The most flexible multiple person duel game is analytically solved and the explicit formulas are solved to determine the time dependent…

Optimization and Control · Mathematics 2021-04-13 Song-Kyoo Kim

Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length,…

Machine Learning · Statistics 2015-03-10 Yarin Gal , Yutian Chen , Zoubin Ghahramani

For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space. For such signals, the developed algorithm is able…

Statistics Theory · Mathematics 2012-10-15 Dave Zachariah , Saikat Chatterjee , Magnus Jansson

Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems such as localisation, mapping, navigation or autonomous…

Machine Learning · Statistics 2019-06-21 Atanas Mirchev , Baris Kayalibay , Maximilian Soelch , Patrick van der Smagt , Justin Bayer

One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly require the derivatives of state data, which are not directly…

Machine Learning · Computer Science 2024-04-17 Dongwei Ye , Mengwu Guo

We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational…

Computation · Statistics 2019-08-07 Gregor Kastner , Sylvia Frühwirth-Schnatter , Hedibert Freitas Lopes

Motivated by the problem of predicting sleep states, we develop a mixed effects model for binary time series with a stochastic component represented by a Gaussian process. The fixed component captures the effects of covariates on the…

Methodology · Statistics 2018-10-23 Xu Gao , Babak Shahbaba , Hernando Ombao

A novel sequential inferential method for Bayesian dynamic generalised linear models is presented, addressing both univariate and multivariate $k$-parametric exponential families. It efficiently handles diverse responses, including…

Methodology · Statistics 2025-01-15 Mariane Branco Alves , Helio S. Migon , Silvaneo V. Santos , Raíra Marotta

In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions. We initiate the study of stochastic optimization for performative prediction.…

Machine Learning · Computer Science 2021-02-22 Celestine Mendler-Dünner , Juan C. Perdomo , Tijana Zrnic , Moritz Hardt

The prior distribution for the unknown model parameters plays a crucial role in the process of statistical inference based on Bayesian methods. However, specifying suitable priors is often difficult even when detailed prior knowledge is…

Methodology · Statistics 2020-03-18 Marcelo Hartmann , Georgi Agiashvili , Paul Bürkner , Arto Klami

A mathematical model for behavioral changes by pair interactions (i.e. due to direct contact) of individuals is developed. Three kinds of pair interactions can be distinguished: Imitative processes, avoidance processes, and compromising…

Statistical Mechanics · Physics 2007-05-23 Dirk Helbing

Mathematical models implemented on a computer have become the driving force behind the acceleration of the cycle of scientific processes. This is because computer models are typically much faster and economical to run than physical…

Computation · Statistics 2021-07-05 Vojtech Kejzlar , Mookyong Son , Shrijita Bhattacharya , Tapabrata Maiti

Credible forecasting and representation learning of dynamical systems are of ever-increasing importance for reliable decision-making. To that end, we propose a family of Gaussian processes (GP) for dynamical systems with linear…

Machine Learning · Computer Science 2025-02-11 Petar Bevanda , Max Beier , Armin Lederer , Alexandre Capone , Stefan Sosnowski , Sandra Hirche

Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings, the available information at…

The pairwise comparison dynamic is a forward ordinary differential equation in a Banach space whose solution is a time-dependent probability measure to maximize utility based on a nonlinear and nonlocal protocol. It contains a wide class of…

Optimization and Control · Mathematics 2025-04-21 Hidekazu Yoshioka

We develop a novel data-driven approach to the inverse problem of classical statistical mechanics: given experimental data on the collective motion of a classical many-body system, how does one characterise the free energy landscape of that…

Statistical Mechanics · Physics 2022-03-01 Peter Yatsyshin , Serafim Kalliadasis , Andrew B. Duncan

This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent…

Robotics · Computer Science 2017-09-26 Maria Bauza , Alberto Rodriguez

Multivariate categorical data are common in many fields. We are motivated by election polls studies assessing evidence of changes in voters opinions with their candidates preferences in the 2016 United States Presidential primaries or…

Methodology · Statistics 2017-08-10 Massimiliano Russo , Daniele Durante , Bruno Scarpa