[Prob] (Coupon collector)

Suppose there are n types of toys, which you are collecting one by one, with the goal of getting a complete set. When collecting toys, the toy types are random (as is sometimes the case, for example, with toys included in cereal boxes or included with kids' meals from a fast food restaurant).

Assume that each time you collect a toy, it is equally likely to be any of the n types. What is the expected number of toys needed until you have a complete set?

Solution: Let N be the number of toys needed; we want to find E(N). Our strategy will be to break up N into a sum of simpler r.v.s so that we can apply linearity. So write

N = N1 + N2 + · · · + Nn,

where N1 is the number of toys until the first toy type you haven't seen before (which is always 1, as the first toy is always a new type), N2 is the additional number of toys until the second toy type you haven't seen before, and so forth.

with

相关推荐
AI科技星1 天前
张祥前统一场论核心场方程的经典验证-基于电子与质子的求导溯源及力的精确计算
线性代数·算法·机器学习·矩阵·概率论
Fleshy数模2 天前
从一条直线开始:线性回归的底层逻辑与实战
人工智能·机器学习·概率论
seeInfinite3 天前
面试常见数学概率题
概率论
木非哲4 天前
AB实验必修课(一):线性回归的深度重构与稳定性评估
线性回归·概率论·abtest
大江东去浪淘尽千古风流人物6 天前
【LingBot-Depth】Masked Depth Modeling for Spatial Perception
人工智能·算法·机器学习·概率论
闪闪发亮的小星星7 天前
主旋参数定义
算法·机器学习·概率论
辰尘_星启10 天前
[最优控制]MPC模型预测控制
线性代数·机器学习·机器人·概率论·控制·现代控制
passxgx10 天前
12.1 均值、方差与概率
算法·均值算法·概率论
Cathy Bryant10 天前
softmax函数与logits
笔记·神经网络·机器学习·概率论·信息与通信
墨上烟雨10 天前
古典概型与几何概型
概率论