论文标题
贝叶斯的缩放与分数布朗运动的推断
Bayesian inference of scaled versus fractional Brownian motion
论文作者
论文摘要
我们提出了一个贝叶斯推理方案，用于缩放布朗尼运动，并研究其在合成数据上的性能，以与分数布朗尼运动结合推理，以进行参数估计和模型选择。我们包括两个模型中测量噪声的可能性。我们发现，对于几百个时间点的轨迹，过程能够很好地解析真实的模型和参数。使用合成数据生成过程的先验也用于推断，该方法基于决策理论是最佳的。我们将使用与数据生成的数据的不同之处进行比较。
We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a prior different from the data generating one.
