Coursera Stochastic Processes 課程筆記, 共十篇:
- Week 0: 一些預備知識
- Week 1: Introduction & Renewal processes
- Week 2: Poisson Processes
- Week3: Markov Chains
- Week 4: Gaussian Processes
- Week 5: Stationarity and Linear filters
- Week 6: Ergodicity, differentiability, continuity
- Week 7: Stochastic integration & Itô formula
- Week 8: Lévy processes
- 整理隨機過程的連續性、微分、積分和Brownian Motion (本文)
根據之前上的 Stochastic processes 課程, 針對以下幾點整理出各自的定義、充分或充要條件:
- 隨機過程的連續性 (Stochastic continuity)
- 隨機過程的微分 (Stochastic differentiability)
- 隨機過程的積分 (Stochasitc Integral)
- Brownian Motion
每次過段時間都忘記, 查找起來也麻煩, 因此整理一篇