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A single trial has probability p of success and (1-p) of failure. For simplicity, we assume p = 1/2 = (1-p). An experiment is defined as a sequences of trials until 4 consecutive success or failures.

Suppose two such experiments were conducted. What is the probability that they will be identical?

Attempt

Given an outcome of experiment 1 that is made up of k trials. Then the probability of experiment 2 being exactly the same is, $ P(k)(1/2^k) $

where P(k) is the probability of an experiment being k trials in length. Since experiment 1 can be of length 4 to infinity, the final probability is, $\sum_{k=4}^{\infty} P(k)^2(1/2^k)$

Question

  1. How do I find P(k)?
  2. Is this the right approach? Or is there a simpler way?
  • 0
    Wrong? No less? Wow... Veeeerry curious to see those results I am! :-) (But please next time, use the @ to signal a comment, I saw yours only by chance although you clearly meant to talk to me.)2012-07-20

1 Answers 1

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Call $T$ the number of trials needed to complete a full experiment. For $1\leqslant k\leqslant3$, call $T_k$ the number of supplementary trials needed to complete an experiment, starting from $k$ identical trials. Thus, $T\stackrel{\text{dist}}{=}1+T_1$, $T_1\stackrel{\text{dist}}{=}1+T_2$ or $1+T_1$ with equal probabilities, $T_2\stackrel{\text{dist}}{=}1+T_3$ or $1+T_1$ with equal probabilities, and $T_3\stackrel{\text{dist}}{=}1$ or $1+T_1$ with equal probabilities.

Fix $|s|\leqslant1$ and call $u_k=\mathrm E(s^{T_k})$. The identities between distributions stated above are translated into the relations $\mathrm E(s^T)=su_1$, $u_1=\frac12s(u_1+u_2)$, $u_2=\frac12s(u_3+u_1)$ and $u_3=\frac12s(1+u_1)$. Solving for $\mathrm E(s^T)$ this system yields $ \mathrm E(s^T)=\frac{s^4}{8-4s-2s^2-s^3}. $ The probability that the first experiment has length $k$ is $\mathrm P(T=k)$. Conditionally on $[T=k]$, the second experiment is identical to the first one with probability $\frac1{2^k}$. Hence, the probability $Q$ that the two experiments are identical is $ Q=\sum\limits_k\mathrm P(T=k)\frac1{2^k}=\left.\mathrm E(s^T)\right|_{s=\frac12}=\frac1{86}.$

  • 0
    *After all* is sweet.2012-07-27