所以这里是按照随机漫步的模式进行建模。
给我一个我可以执行的，随机漫步的例子，而且你还知道答案。
Give me the simplest example of a simulation of the random walk I could run, where you're confident you know the answer.
我们所做的，就是在一个人造的例子中，引入了随机漫步的概念。
What we've done, is we've introduced the notion of a random walk in the context of a pretty contrived example.
但是，近年来均值回归理论对随机漫步理论提出了挑战。
But, in recent years, Average Value Return Theory gave a challenge to the Random Walk Theory.
以上这些行为极大的破坏了泛洪和随机漫步的搜索性能。
These actions greatly damaged the flooding and random walk search performance.
他们在生物学中经常使用到随机漫步,比方说建立动力模型。
They use it a lot in biology to do things like model kinetics.
当我们观察随机漫步序列时，我认为代表性原则偏误，也起了一定作用。
When we looked at the random walk series I think the representativeness heuristic played a role in there as well.
消息也已经无法对股票产生影响，股价的波动已经不是随机漫步模式。
News has also been unable to have an impact on the stock, price volatility is not the random walk model.
随机漫步理论认为股票的价格是不能预测的，许多实证检验的结果也支持了这一结论。
Random Walk Theory thinks that the price of the shares is unpredictable, and many concrete examples have already proved this conclusion.
让我们从随机漫步的神坛上下来，某种意义上说，甚至是有些荒谬的，让我们来讲讲绘图的实际句法。
So we've gone from the sublime, of what random walks are good for, to in some sense the ridiculous, the actual syntax for plotting things.
问题2：非稳定型随机漫步的连续极限，具调合井的随机漫步，具备巨大尾部的漫步，鞍点近似解。
Problem Set2: Continuum approximations of non-stationary random walks, random walk in a harmonic well, steps with fat tails, saddle-point asymptotics.
所以现在我们可以总结一下了，这实际上就是正确的结论，我们知道这个随机漫步的酒鬼，将要走上500步。
And so now we can conclude, and would actually be the correct conclusion, that we know about how far this random drunk is going to move in 500 steps.
有一本很有名的书叫做，《随机漫步在华尔街上》，书中说道一些以概率a发生的事物，能够用随机漫步法进行模拟。
There was a very famous book called a random walk Down Wall Street, that argued that things happened as a, random walk was a good way to model things.
RSR在随机漫步的基础上通过考虑邻居节点的热度改进请求转发方式，FRSR通过结合洪泛搜索改进随机漫步转发策略。
Based on random walk, RSR modifies request-forward manner by taking the popularity of neighbor nodes into consideration on passing searching request. FRSR combines flooding search with random walk.
传统的局部信任模型采用简单洪泛的方法获得信任信息，针对该方法效率较低且对网络资源消耗较大的问题，提出一种基于随机漫步的搜索信任路径的算法。
This paper presents a trust path search algorithm based on random walk, which is able to improve the search by using the path information in the past.
其想法在于，股票价格遵循偏离均衡的相当温和的随机漫步（random walk）原则，颇似布朗运动（Brownian motion）[5]中四处轻摆的花粉微粒[6]。
The idea is that share prices follow some gentle random walk away from an equilibrium, rather like motes of dust jiggling around in Brownian motion.
其想法在于，股票价格遵循偏离均衡的相当温和的随机漫步（random walk）原则，颇似布朗运动（Brownian motion）[5]中四处轻摆的花粉微粒[6]。
The idea is that share prices follow some gentle random walk away from an equilibrium, rather like motes of dust jiggling around in Brownian motion.
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