问题
A walker tosses a fair coin every minute: heads, one step east; tails, one step west. Is she certain to come back to where she started? What if she can also step north and south — or up and down in space?
先动手做实验,再往下读。探索没有错误的方式。
实验
概率实验室
每个行走者随机向左或向右走一步。离起点的典型距离按 √n 增长。
- 其中 8 条游走
- 均方根距离(200 条游走)
- ±√n(精确)
走 100 步后的均方根距离
模拟值10.94
精确值√100 ≈ 10
模拟 200 条 100 步的游走:均方根距离为 10.94。精确值是 √n,约 10。
是证据,不是证明这是使用带种子的随机数生成器对 200 条随机游走的模拟(第 1 次运行)。它是证据,不是证明:精确值来自数学,重新运行会得到略有不同的数字。
精确地说:走 n 步后(位置)² 的平均值是 n,所以均方根距离是 √n。要走到两倍远,需要四倍的步数。
提示(如果你需要)
这是怎么回事
这是怎么回事
George Pólya answered this in 1921. On a line (one dimension) or a grid of streets (two dimensions), the walker returns home with probability 1 — in fact infinitely often. In three dimensions she does not: the chance of ever coming home is only about . A remark often attributed to Shizuo Kakutani puts it this way: a lost person will find their way home, but a lost bird may not.
The reason is a sum. A walk returns home infinitely often exactly when the expected number of visits home is infinite, and that expected number is the sum, over all times, of the chance of being home then. After steps the chance of being home behaves roughly like
The first two sums diverge — the second only just, like the harmonic series — so the walker comes home again and again. The third converges, so she may wander off for ever.
There is a surprise even in one dimension: the walker is certain to return, yet the average time she takes to return is infinite. Random walks like these model the diffusion of molecules, stock prices, the spread of heat, and the search patterns of animals.
注
The simulation shows a finite number of walks for a finite time — evidence. Pólya's theorem is a proof about every walk forever.
由此通向何处
- 本科高年级 · 课程已上线Markov chainsDiscrete-time Markov chains: transition matrices, classification of states, stationary distributions, convergence to equilibrium, hitting times and random walks.
- 研究生一年级 · 课程已上线Stochastic processesContinuous-time processes: Poisson processes, continuous-time Markov chains, Brownian motion and its path properties, continuous-time martingales, and an introduction to Itô calculus and stochastic differential equations.
- 研究路径 · 课程已上线Random structures: walks, percolation and random geometryOptional research direction: randomness creating shape. Random walks on graphs and Pólya's recurrence theorem, electrical networks, percolation and phase transitions, random graphs, Brownian motion and its fractal paths, and a glimpse of Schramm–Loewner evolution and random planar geometry.