ArticlePhysical review. E2023
Bézier interpolation improves the inference of dynamical models from data.
Article in Physical review. E, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Article
- Article
- A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Efficient epistasis inference via higher-order covariance matrix factorization.bioRxiv : the preprint server for biology · 2024Article
- A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.bioRxiv : the preprint server for biology · 2024Article
- Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data.Molecular biology and evolution · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Many dynamical systems, from quantum many-body systems to evolving populations to financial markets, are described by stochastic processes. Parameters characterizing such processes can often be inferred using information integrated over stochastic paths. However, estimating time-integrated quantities from real data with limited time resolution is challenging. Here, we propose a framework for accurately estimating time-integrated quantities using Bézier interpolation. We applied our approach to two dynamical inference problems: Determining fitness parameters for evolving populations and inferring forces driving Ornstein-Uhlenbeck processes. We found that Bézier interpolation reduces the estimation bias for both dynamical inference problems. This improvement was especially noticeable for data sets with limited time resolution. Our method could be broadly applied to improve accuracy for other dynamical inference problems using finitely sampled data.
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