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concepts/stochastic-differential-equation.md
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title: "随机微分方程 (Stochastic Differential Equation)"
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created: 2026-06-17
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updated: 2026-06-17
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type: concept
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tags: [mathematics, stochastic-processes, theory, probability]
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sources: [raw/papers/tiwari-ticks-to-flows-2026.md]
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confidence: high
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---
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# 随机微分方程 (Stochastic Differential Equation)
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SDE 是描述**受随机噪声驱动的连续时间动态系统**的数学框架,是 [[ticks-to-flows|Tiwari et al. (2026)]] 论文的核心数学工具。
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## 标准形式
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```
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dX_t = b(X_t) dt + σ(X_t) dW_t
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```
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- `b(X_t) dt`:**漂移项**(drift),确定性的变化方向
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- `σ(X_t) dW_t`:**扩散项**(diffusion),随机波动
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- `W_t`:[[wiener-process|Wiener 过程]](Brownian motion)
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## Itô 积分
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SDE 的解通过 [[ito-calculus|Itô 积分]] 定义:
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```
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X_t = X_0 + ∫_0^t b(X_l) dl + ∫_0^t σ(X_l) dW_l
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```
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在适当的条件(Lipschitz 连续 + 线性增长)下,解在概率意义下存在且唯一。
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## 在强化学习中的应用
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在 [[continuous-time-rl|连续时间 RL]] 中,SDE 用于建模:
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1. **环境转移**:`ds_t = (g(s_t) + h(s_t)a_t)dt + σ(s_t)dW_t`
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2. **探索动力学**:同时包含策略随机性和环境随机性
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3. **梯度时间动态**:描述参数更新如何改变状态分布
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## 关键性质
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- **鞅性质**:扩散项形成一个[[martingale-clt|鞅]],可用于 CLT 分析
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- **Markov 性**:未来仅依赖当前状态
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- **无穷小生成元**(infinitesimal generator)L^π 刻画函数沿轨道的瞬时变化
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## 参考
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- [[wiener-process|维纳过程]]
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- [[ito-calculus|Itô 微积分]]
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- [[continuous-time-rl|连续时间 RL]]
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- [[exploratory-dynamics|探索动力学]]
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- [[ticks-to-flows|Ticks to Flows]]
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