27 papers · ranked by Valyu relevance
Genshiro Kitagawa
The non-Gaussian filter provides a deterministic numerical method for nonlinear and non-Gaussian state-space models, but its application has long been limited due to the computational cost of numerical integration. Advances in computing power and memory capacity have substantially reduced this limitation for low and…
Sezen Cekic, Didier Grandjean, Olivier Renaud
Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer theoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic that may vary in time in order to evaluate the…
Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou
Models Authors: Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary…
Rubén Loaiza‐Maya, Didier Nibbering
Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state space models. This paper proposes a variational approximation that is accurate and fast for any model with a closed-form measurement…
Kostas Tsampourakis, Víctor Elvira
State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the estimation of latent states and parameters. In this work, we focus on parameter inference, which for SSMs is in general a very challenging problem due to the intractability…
Inesh Munaweera, Les N. Harris, Jean-Sébastien Moore, Ross F. Tallman + 6 more
Hierarchical modelling is frequently used to model ecological processes because of its ability to handle complex ecological phenomena by decomposing them into naturally explainable sub-models. Hierarchical Bayesian approaches have gained widespread use in health, social, and environmental sciences, including in the…
Jaakko Luttinen, Tapani Raiko, Alexander Ilin
This paper introduces a linear state-space model with timevarying dynamics. The time dependency is obtained by forming the state dynamics matrix as a time-varying linear combination of a set of matrices. The time dependency of the weights in the linear combination is modelled by another linear Gaussian dynamical model…
Arnab Bhattacharya, Simon Wilson
Dynamic state-space models (Durbin and Koopman, 2001), consisting of an unknown state Markov process X0, X1, . . . and noisy observations of that process Y1, Y2, . . . that are conditionally independent, are used in a wide variety of applications e.g. wireless networks (Haykin et al., 2004), object tracking (Ristic et…
Mingjian He, Proloy Das, Gladia Hotan, Patrick L. Purdon
Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting…
Rutger‐Jan Lange
We present a new filter for state-space models based on Bellman's dynamic programming principle, allowing for nonlinearity, non-Gaussianity and degeneracy in the observation and/or state-transition equations. The proposed Bellman filter is a direct generalisation of the (iterated and extended) Kalman filter, but…
Joshua A Cullen, Caroline L Poli, Robert J Fletcher, Denis Valle
Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioral states to help understand why animals move across the landscape (or seascape). While there are a variety of methods that make behavioral inference from biotelemetry data, some…
Edward Lavender, Carlo Albert, Andreas Scheidegger
Animal geolocation is the core of movement ecology. In aquatic ecosystems, electronic tagging and tracking technologies, such as passive acoustic telemetry systems and biologging sensors, are widely deployed. However, statistical estimation of individual locations from these datasets can be challenging and…
S. Gerber, L. Pospisil, M. Navandar, I. Horenko
Finding reliable discrete approximations of complex systems is a key prerequisite when applying many of the most popular modeling tools. Common discretization approaches (for example, the very popular K-means clustering) are crucially limited in terms of quality and cost. We introduce a low-cost improved-quality…
Teague R. Henry, Lindley R. Slipetz, Ami Falk, Jiaxing Qiu + 1 more
Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day)…
Brandon Robinson, Philippe Bisaillon, Rimple Sandhu, Mohammad Khalil + 8 more
A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential…
L. Mark Berliner, Radu Herbei, Christopher K. Wikle, Ralph F. Milliff + 1 more
'Ralph F. Milliff' 'Pablo Martin Rodriguez'] Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We…
Samer A. Kharroubi
Background Conventionally, models used for health state valuation data have been frequentists. Recently a number of researchers have investigated the use of Bayesian methods in this area. The aim of this paper is to put on the map of modelling a new approach to estimating SF-6D health state utility values using…
Devin S. Johnson, Jeff L. Laake, Sharon R. Melin, Robert L. DeLong
State-based Cormack-Jolly-Seber (CJS) models have become an often used method for assessing states or conditions of free ranging animals through time. Although originally envisioned to account for differences in survival and observation processes when animals are moving though various geographical strata, it has…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Farzana Jahan, Daniel W. Kennedy, Earl W. Duncan, Kerrie L. Mengersen + 1 more
'Kerrie L. Mengersen' 'Cathy W. S. Chen'] Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Authors not listed
Thorough treatment of conformation in computational chemistry is required to capture the subtle energy differences that lead to experimental observations. Accurate quantum chemistry calculations are very expensive and evaluation of the entire ensemble found during a conformational search is often unachievable. This is…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…