Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak-Czochra, Zuzanna Szymańska · 2025
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Description
Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak-Czochra, and Zuzanna Szymańska present a new framework for fitting math models of living systems to data, in a methods paper from 2025. Calibrating such models is hard: data are scarce and noisy, and the models are costly to run. Classic Bayesian tools strain on systems with mixed or chaotic behavior.
Their method, built on a correlation integral likelihood, handles pattern-forming and individual-based models in one frame. They show its range through sample uses in math biology. They also compare random sampling with guided gradient-flow sampling, and show how the two blend. The result is a practical toolbox for model fitting.
Catalog record
| CREATOR | Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak-Czochra, Zuzanna Szymańska |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 155 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |