# Parity of the sum of consecutive Bernoulli random variables

$\newcommand{\Var}{\operatorname{Var}}$I have $X_1,X_2,\ldots,X_{n+1}$ i.i.d. rv, each $X_i$ is a Bernoulli rv with parameter $p$, i.e.

$X_i \in \{0,1\}$, $P(X_i=0)=1-p$ and $P(X_i=1)=p$ with $0 \leq p \leq 1$

I define the rvs $Y_i$ for $i=1,2,\ldots,n$ as

• $Y_i=0$ if $X_i+X_{i+1}$ is even
• $Y_i=1$ if $X_i+X_{i+1}$ is odd

then I define $S=Y_1+Y_2+\cdots+Y_n$

I have to calculate $E(S)$ and $\Var(S)$.

First I calculate the distribution of $Y_i$

\begin{align} P(Y_i=0) & =P(X_i=0,X_{i+1}=0) + P(X_i=1,X_{i+1}=1) \\[6pt] & =P(X_i=0)P(X_{i+1}=0)+P(X_i=1)P(X_{i+1}=1) \\[6pt] & =(1-p)^2+p^2 \end{align}

$P(Y_i=1)=1-P(Y_i=0)=2p(1-p)$

then $E(Y_i)=2p(1-p)$ and using the linearity of the mean

$E(S)=nE(Y_i)=2np(1-p)$

But for $\Var(S)$? I can't use that $\Var(S)=\Var(Y_1)+\Var(Y_2)+\cdots+\Var(Y_n)$ because the $Y_i$s are dipendent. Maybe could be useful that $Y_i=|X_i-X_{i+1}|$?

• Just try to calculate $cov(X_i,X_j)$ and from that calculate $cov(Y_i,Y_j)$ – Snufsan Aug 21 '14 at 13:24
• I see two answers posted so far: One gives (or seems to attempt to give) every detail and the other gives the essential main ideas, after which working out the details is routine. – Michael Hardy Aug 21 '14 at 13:54
• A useful formulation of $Y_i$ is $$Y_i=\mathbf 1_{X_i\ne X_{i+1}}.$$ – Did Aug 21 '14 at 13:55
• @MichaelHardy: That sounds a little bit rude. Do you have something to point out in my answer that is wrong? If you do, say so and I'll fix it. Our answers are identical when it comes to ideas, mine just gives the details as well. – J. J. Aug 21 '14 at 13:57
• @J.J. : I wrote "or seems to attempt to give" only because I hvaen't checked it in detail yet. – Michael Hardy Aug 21 '14 at 13:57


The variance of a Bernoulli random variable is given by $\var(Y) = (\mathbb E Y)(1-\mathbb E Y)$.

The covariance between two Bernoulli random variables is given by $\cov(Y_i,Y_j)$ $= \mathbb E(Y_i Y_j) - (\mathbb E Y_i)(\mathbb E Y_j)$.

There are $n-1$ unordered pairs $i,j$, and hence $2(n-1)$ ordered pairs $(i,j)$, in which the covariance is not $0$, and the same covariance occurs in each of those, so the variance is just $n\var(Y_1)+2(n-1)\cov(Y_1,Y_2)$.

Notice that $\mathbb E(Y_1 Y_2)= \Pr(Y_1=Y_2=1)=\Pr((X_1,X_2,X_3)\in\{(0,1,0),(1,0,1)\})$.

• Please replace $n-1$ by $2(n-1)$. – Did Aug 21 '14 at 13:42

By your result we have $Var(S) = E[S^2] - E[S]^2 = E[S^2] - 4 n^2 p^2 (1 - p)^2$. Now $$E[S^2] = E[Y_1^2 + \dots + Y_n^2 + 2 \sum_{1 \le i < j \le n} Y_iY_j].$$ By the distribution you calculated we have $E[Y_i^2] = 2p(1-p)$, so $$E[S^2] = 2 n p (1 - p) + 2 \sum_{1 \le i < j \le n} E[Y_iY_j].$$ Next notice that $Y_i$ and $Y_j$ are independent if $j \ge i+2$. In this case we have $E[Y_i Y_j] = E[Y_i] E[Y_j] = 4 p^2 (1-p)^2$. If $j = i+1$, then $Y_i$ and $Y_j$ are not independent, and we have to calculate the distribution of $Y_i Y_{i+1}$. We have $$P(Y_i Y_{i+1} = 1) = P(X_i = 0)P(X_{i+1} = 1)P(X_{i+2} = 0) + P(X_i = 1)P(X_{i+1} = 0)P(X_{i+2} = 1) = (1-p)^2 p + p^2 (1-p) = (1-p) p,$$ so $E[Y_i Y_{i+1}] = (1-p) p$. Thus $$E[S^2] = 2n p(1-p) + 2 \left( {n \choose 2} 4p^2 (1-p)^2 - (n-1) 4 p^2 (1-p)^2 + (n-1) (1-p) p\right),$$ which we can simplify to $$E[S^2] = 2(2n-1)p(1-p) + 4(n-1)(n-2)p^2(1-p)^2,$$ so the variance is $$Var[S] = p(1-p)(4(n-1)(n-2) p(1-p) + 2(2n-1) - 4n^2p(1-p)) = 2p(1-p)(2n - 1 - 2(3n-2)p(1-p)).$$

• Thanks for the detailed explanation. Just I can't get the last step. I have to calculate $\sum_{1 \leq i < j \leq n} E(Y_i Y_j)$. For each $Y_i$ I have $n-1$ terms: $E(Y_iY_{i+1})$ and $n-2$ times $E(Y_i Y_j)$ with $j \leq i+2$; then $i=1,...,n$ So i think the sum should be $n[p(1-p)+(n-2)4p^2(1-p)^2]$ Is it correct? – Louis Aug 21 '14 at 16:36
• @Louis: For fixed $i$ we have actually $n-i$ terms: $E(Y_i Y_{i+1})$ and $n-i-1$ terms $E(Y_i Y_j)$ with $i+2 \le j \le n$, so the sum should be $\sum_{i=1}^{n-1} (p(1-p) + (n-i-1) 4p^2(1-p)^2)$. – J. J. Aug 21 '14 at 19:35
• @Louis: There was a calculation error in my answer though, which I fixed. – J. J. Aug 21 '14 at 19:47