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原文連結
論文資訊
- 類型：已發表論文
- 日期：2024-10-09
摘要
We investigate the impact of noise on parameter fitting for an Ornstein-Uhlenbeck process, focusing on the effects of multiplicative and thermal noise on the accuracy of signal separation. To address these issues, we propose algorithms and methods that can effectively distinguish between thermal and multiplicative noise and improve the precision of parameter estimation for optimal data analysis. Specifically, we explore the impact of both multiplicative and thermal noise on the obfuscation of the actual signal and propose methods to resolve them. First, we present an algorithm that can effectively separate thermal noise with comparable performance to Hamilton 蒙地卡羅 (HMC) methods, but with significantly improved speed. We then analyze multiplicative noise and demonstrate that HMC is insuffic
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