# Probability of error for detection problem

Let $$X \in \mathbb{R}^N$$ and $$Z \sim \mathcal{N}(0, \sigma^2 I)$$ be random vectors.

$$Y = X + Z$$

$$X$$ can be either $$a_0 \in \mathbb{R}^N$$ or $$a_1\in \mathbb{R}^N$$ with equal probability. So the decision rule is $$||y - a_0||^2 \overset{X = a_1}{\underset{X = a_0}{\gtrless}} ||y - a_1||^2$$

What is $$P(\text{error} | X = a_0)$$?

My attempt at solution:

\begin{align*} P(\text{error} | X = a_0) &= P(||Y - a_0||^2 > ||Y - a_1||^2 | X = a_0)\\ &= P(||Y - a_0||^2 > ||Y - a_1||^2) \, \, \text{ where Y \sim \mathcal{N}(a_0, \sigma^2 I) }\\ &= \frac{1}{(2 \pi)^{N/2}} \frac{1}{\sigma^N} \int_{D} \exp \left( -\frac{1}{2 \sigma^2} ||y - a_0||^2 \right) dy \end{align*} where $$D \subseteq \mathbb{R}^N$$ is the region containing all points closer to $$a_1$$ than $$a_0$$.

Of course, this integral does not have an analytic formula, but can this be written in terms of single dimensional CDFs, exploiting the fact that $$Y$$'s components are independent random variables.

• so, what is your question? Btw, your result looks fine, aside from the integral missing the measure indicator, but you can't just "omit" the conditionla $|X=a_o$ like this and redefine $Y$. Be clean, and the answer will jump at you! (Hint: insert your known$X$ into $\|Y-a_0= X + N - a_o\|^2> \ldots$) – Marcus Müller Feb 14 at 14:33
• Also, the "so the decision rule is" is a bit ... hm. Where does that come from? Don't just claim that (it's right, here!), but understand why that is. – Marcus Müller Feb 14 at 14:35
• in 2d space draw the points and draw a line between them like the other poster said. You will end up integrating over a half plane IIR. – FourierFlux Feb 15 at 3:51

Hint: do a change of variables so that the points $$a_0$$ and $$a_1$$ lie on one axis in $$n$$-space. This is a standard method that is used repeatedly in analysis of digital communication systems and it is good to get a handle on it right away.
\begin{align*}||Y - a_0||^2 &\overset{X = a_1}{\underset{X = a_0}{\gtrless}} ||Y - a_1||\\ \end{align*} can be written as: \begin{align*}(a_1 - a_0)^T Y &\overset{X = a_1}{\underset{X = a_0}{\gtrless}} \frac{||a_1||^2 - ||a_0||^2}{2}\\ \end{align*}
Note that $$g(Y) = \left(a_1 - a_0 \right)^T Y$$ is a sufficient statistic and a scalar quantity. You can prove that $$\left ( g(Y) | X = a_0 \right) \sim \mathcal N \left( (a_1 - a_0)^T a_0, \sigma^2 ||a_1 - a_0||^2 \right)$$
Hence, \begin{align*}P(\text{error} | X = a_0) &= P\left( g(Y) > \frac{||a_1||^2 - ||a_0||^2}{2} \Bigg | X = a_0\right) \end{align*}