实现可微分粒子滤波工具包，让复杂时序建模优化更简单。
PyDPF: A Python Package for Differentiable Particle Filtering
- 基于PyTorch构建统一接口，使粒子滤波支持梯度计算。
- 复现多个现有研究实验，验证框架有效性。
- 适合时序建模、状态估计与参数优化的研究者使用。
状态空间模型（SSMs）在时间序列分析中广泛应用。面对真实数据中的复杂系统，常采用粒子滤波（PF）这一高效的蒙特卡洛方法来估计观测序列对应的隐藏状态。应用粒子滤波需指定系统的参数形式和参数值，但这些通常未知，必须通过估计获得。标准粒子滤波无法直接使用基于梯度的优化方法，因其本身不可微。近年来，若干方法通过修改重采样步骤，使粒子滤波具备可微性。本文提出一个基于主流PyTorch框架的统一API实现，包含多种可微分粒子滤波（DPF）算法。该实现使这些算法对更广泛的研究群体易于获取，并便于相互比较。我们通过复现多个已有研究的实验验证了框架的有效性，并展示了DPFs在应对状态空间建模常见挑战中的应用潜力。
原文摘要 · Abstract (English)
State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an efficient Monte Carlo method for estimating the hidden state corresponding to a sequence of observations. Applying particle filtering requires specifying both the parametric form and the parameters of the system, which are often unknown and must be estimated. Gradient-based optimisation techniques cannot be applied directly to standard particle filters, as the filters themselves are not differentiable. However, several recently proposed methods modify the resampling step to make particle filtering differentiable. In this paper, we present an implementation of several such differentiable particle filters (DPFs) with a unified API built on the popular PyTorch framework. Our implementation makes these algorithms easily accessible to a broader research community and facilitates straightforward comparison between them. We validate our framework by reproducing experiments from several existing studies and demonstrate how DPFs can be applied to address several common challenges with state space modelling.
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