AI 中文总结
本研究提出贝叶斯框架，结合线性高斯模型精确似然与非线性动力学近似似然，实现仅位置观测下二阶Langevin动力学的模型比较，经合成数据与盘基网柄菌轨迹验证，可可靠恢复模型且结果具统计支持性。
AI 中文摘要
我们开发了一种贝叶斯框架，用于仅从位置轨迹进行二阶Langevin动力学的模型比较。虽然此前已针对仅位置的非线性推理构建了近似增量似然，但在位置观测下比较多个二阶模型的统一证据框架仍有所欠缺。本研究通过结合线性高斯模型的精确增量似然与先前提出的非线性动力学近似似然，解决了该问题。合成数据基准测试表明，在精细采样间隔下可可靠恢复生成模型，而在粗糙时间采样下可识别性逐步丧失。对盘基网柄菌（Dictyostelium discoideum）轨迹的应用显示，统计支持的模型强烈依赖于时间分辨率。此外，所选模型再现了实验轨迹的关键统计特性，为模型比较结果提供了额外支持。因此，本框架为部分观测的随机动力学提供了一种实用的基于证据的比较方法。
英文摘要
We develop a Bayesian framework for model comparison of second-order Langevin dynamics from position-only trajectories. While approximate increment likelihoods for nonlinear position-only inference have been formulated previously, a unified evidence-based framework for comparing multiple second-order models under positional observation has remained lacking. Here we address this problem by combining exact increment likelihoods for linear Gaussian models with a previously proposed approximate likelihood for nonlinear dynamics. Synthetic-data benchmarks show reliable recovery of the generating model at fine sampling intervals and progressive loss of identifiability under coarse temporal sampling. Application to Dictyostelium discoideum trajectories demonstrates that the statistically supported model depends strongly on temporal resolution. Moreover, the selected models reproduce key statistical properties of the experimental trajectories, providing additional support for the model-comparison results. Our framework therefore offers a practical approach to evidence-based comparison of partially observed stochastic dynamics.
Commentsmain: 19 pages, 11 figures / SI: 14 pages, 8 figures
