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Asymptotic optimality is a key theoretical property in model averaging. Due to technical difficulties, existing studies rely on restricted weight sets or the assumption that there is no true model with fixed dimensions in the candidate set.…

Statistics Theory · Mathematics 2024-11-15 Wenchao Xu , Xinyu Zhang

Implicit sampling is a weighted sampling method that is used in data assimilation, where one sequentially updates estimates of the state of a stochastic model based on a stream of noisy or incomplete data. Here we describe how to use…

Numerical Analysis · Mathematics 2016-01-20 Matthias Morzfeld , Xuemin Tu , Jon Wilkening , Alexandre J. Chorin

Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with high-dimensional data. One possible way to scale this…

Machine Learning · Statistics 2024-02-02 Bernardo Fichera , Viacheslav Borovitskiy , Andreas Krause , Aude Billard

We consider the problem of best subset selection (BSS) under high-dimensional sparse linear regression model. Recently, Guo et al. (2020) showed that the model selection performance of BSS depends on a certain identifiability margin, a…

Statistics Theory · Mathematics 2025-04-15 Saptarshi Roy , Ambuj Tewari , Ziwei Zhu

Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the…

Data Analysis, Statistics and Probability · Physics 2021-01-14 Stefan Wunsch , Simon Jörger , Roger Wolf , Günter Quast

If an experimentalist observes a sequence of emitted quantum states via either projective or positive-operator-valued measurements, the outcomes form a time series. Individual time series are realizations of a stochastic process over the…

Quantum Physics · Physics 2023-06-14 A. Venegas-Li , J. P. Crutchfield

Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown…

Instrumentation and Methods for Astrophysics · Physics 2025-07-18 Harry Desmond

Filtered Poisson processes are often used as reference models for intermittent fluc- tuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical…

Data Analysis, Statistics and Probability · Physics 2018-05-04 Audun Theodorsen , Odd Erik Garcia , Martin Rypdal

Complex systems are found in most branches of science. It is still argued how to best quantify their complexity and to what end. One prominent measure of complexity (the statistical complexity) has an operational meaning in terms of the…

Data Analysis, Statistics and Probability · Physics 2011-10-24 Karoline Wiesner , Mile Gu , Elisabeth Rieper , Vlatko Vedral

Optimization problems with the objective function in the form of weighted sum and linear equality constraints are considered. Given that the number of local cost functions can be large as well as the number of constraints, a stochastic…

Optimization and Control · Mathematics 2026-05-26 Nataša Krejić , Nataša Krklec Jerinkić , Sanja Rapajić , Luka Rutešić

In various practical situations, we encounter data from stochastic processes which can be efficiently modelled by an appropriate parametric model for subsequent statistical analyses. Unfortunately, the most common estimation and inference…

Methodology · Statistics 2022-04-12 Rohan Hore , Abhik Ghosh

Finding parameters that minimise a loss function is at the core of many machine learning methods. The Stochastic Gradient Descent algorithm is widely used and delivers state of the art results for many problems. Nonetheless, Stochastic…

Machine Learning · Computer Science 2018-09-26 Yao Zhang , Andrew M. Saxe , Madhu S. Advani , Alpha A. Lee

We propose a new framework to estimate the evolution of an ensemble of indistinguishable agents on a hidden Markov chain using only aggregate output data. This work can be viewed as an extension of the recent developments in optimal mass…

Optimization and Control · Mathematics 2021-07-01 Isabel Haasler , Axel Ringh , Yongxin Chen , Johan Karlsson

It is known, that an $\epsilon$-machine is either exactly or asymptotically synchronizing. In the exact case, the observer can infer the current machine state after observing $L$ generated symbols with probability $1-a^L$ where $0 \leq a<1$…

Information Theory · Computer Science 2014-09-24 Mikhail V. Berlinkov

Fabrication process variations are a major source of yield degradation in the nano-scale design of integrated circuits (IC), microelectromechanical systems (MEMS) and photonic circuits. Stochastic spectral methods are a promising technique…

Computational Engineering, Finance, and Science · Computer Science 2016-11-08 Zheng Zhang , Tsui-Wei Weng , Luca Daniel

Recovering dynamical equations from observed noisy data is the central challenge of system identification. We develop a statistical mechanics approach to analyze sparse equation discovery algorithms, which typically balance data fit and…

Statistical Mechanics · Physics 2025-09-16 Andrei A. Klishin , Joseph Bakarji , J. Nathan Kutz , Krithika Manohar

We recount recent history behind building compact models of nonlinear, complex processes and identifying their relevant macroscopic patterns or "macrostates". We give a synopsis of computational mechanics, predictive rate-distortion theory,…

Statistical Mechanics · Physics 2014-12-31 James P. Crutchfield , Ryan G. James , Sarah Marzen , Dowman P. Varn

A hidden Markov model with trends is a hidden Markov model whose emission distributions are translated by a trend that depends on the current hidden state and on the current time. Contrary to standard hidden Markov models, such processes…

Statistics Theory · Mathematics 2021-12-17 Luc Lehéricy , Augustin Touron

In the context of a species sampling problem we discuss a non-parametric maximum likelihood estimator for the underlying probability mass function. The estimator is known in the computer science literature as the high profile estimator. We…

Statistics Theory · Mathematics 2018-01-12 Dragi Anevski , Richard D. Gill , Stefan Zohren

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g.…

Numerical Analysis · Computer Science 2013-03-19 Bojana V. Rosić , Anna Kučerová , Jan Sýkora , Oliver Pajonk , Alexander Litvinenko , Hermann G. Matthies