Parameter estimation in Conditional Sequential Monte Carlo algorithms through Particle Learning

2026-08-28

AI Summary

本文通过提出p-CSMC算法，结合参数学习和祖先采样，解决了条件序列蒙特卡洛算法中静态参数和潜在状态联合估计的问题，提高了在强内部相关性情况下的探索效率。

Abstract

In this work, we explore particle learning strategies for the joint estimation of static parameters and latent states within conditional sequential Monte Carlo (CSMC) algorithms. Building on this idea, we propose the p(parameter)-CSMC algorithm, which incorporates both parameter learning and ancestor sampling, leading to much better mixing properties compared to (particle) Gibbs sampling in settings where strong internal correlations may challenge effective exploration. We also include two applications in the context of a branching process model: one using synthetic data, where we estimate the infectivity profile while assuming the reproductive number to be known, and another using real data, where we address the joint inference of the reproductive number and the infectivity profile based on daily hospital incidence from the arrival of the SARS-CoV-2 lineage B.1.1.7 (Alpha) in Norway in February 2021. We show that, in these settings, performance is dramatically enhanced, with substantially faster mixing and markedly reduced autocorrelation compared with standard particle Gibbs.

Problem

• Conditional Sequential Monte Carlo
• Parameter Estimation
• Latent States
• Internal Correlations

Innovation

• particle learning
• CSMC
• ancestor sampling
• parameter estimation
• latent states