Bayes Optimal Decision Boundaries for Gaussian Data with Equal Covariance I am drawing samples from two classes in the two-dimensional Cartesian space, each of which has the same covariance matrix $[2, 0; 0, 2]$.  One class has a mean of $[1.5, 1]$ and the other has a mean of $[1, 1.5]$.  If the priors are $4/7$ for the former and $3/7$ for the later, how would I derive the equation for the ideal decision boundary?

If it turns out that misclassifying the second class is twice as expensive as the first class, and the objective is to minimize the expected cost, what equation would I use for the best decision boundary?
When class-conditional distributions are gaussian with equal covariance matrices, the optimal decision boundary is a hyperplane.  This is the core concept behind Linear Discriminant Analysis (LDA).

For any data point $x$, the probability that $x$ comes from class $\omega_1$ is: 

$P(x|\omega_1) \sim N(\mu_1,\Sigma) = (2\pi)^{-1}|\Sigma|^{-1/2}\exp\left\{ (-1/2) (x-\mu_1)'\Sigma^{-1}(x-\mu_1)
\right\}$

Similarly, $P(x|\omega_2) \sim N(\mu_2,\Sigma)$.

Let's denote the prior probability of class 1 and 2 as $P(\omega_1)$ and $P(\omega_2)$, respectively.

Equal Misclassification Cost:

If the cost of misclassification is equal, we want to assign new data points such that the probability of misclassification is minimized. This decision rule assigns points to class 1 when $x$ satisfies:

$P(x|\omega_1)P(\omega_1) &gt; P(\omega_2)(P(x|\omega_2)$

We can establish a similar rule for assigning points to class 2. 

o find the decision boundary, we need to find the values of $x$ that satisfy

$P(x|\omega_1)P(\omega_1) = P(x|\omega_2)P(\omega_2)$

The values of $x$ that satisfy this equality here lie on a line (or hyperplane for higher dimensions).

Unequal Misclassification Cost:

For the second question, you will need an additional term.  Let's call $C(\omega_1)$ the cost of making an error when the data point was actually from class 1 and $C(\omega_2)$ the cost of making an error when the data point was from class 2.

To minimize the cost of an error, your new decision rule would be to assign $x$ to class 1 when $x$ satisfies:

$P(x|\omega_1)P(\omega_1)C(\omega_1) &gt; P(x|\omega_2)P(\omega_2)C(\omega_2)$

As before, to find the decision boundary, you need to solve for $x$ when:

$P(x|\omega_1)P(\omega_1)C(\omega_1) = P(x|\omega_2)P(\omega_2)C(\omega_2)$

This decision boundary will still be a line / hyperplane, however it may have a different offset or orientation from the solution in the case of equal misclassification cost.