# Likelihood of the mean of one random variable with unknown parameters greater than another

Assume we have two random variables $X$ and $Y$ that are gamma distributed (or normally distributed, if it makes the math easier) with unknown parameters. We have samples $x_1,x_2,...,x_m$ and $y_1,y_2,...,y_n$ where $m$ and $n$ are small and may not be equal. What is the best way to determine if the mean of $X$ is greater than the mean of $Y$? I assume one possible way is to estimate the parameters of $Z=X-Y$ and compute $P(Z>0)$, but I don't want to throw away samples if $m \neq n$. Another way is to estimate the parameters of $X$ and $Y$ and compute $P(X>Y)$. What calculation is the most accurate, most meaningful, and best use of the data?

Edit:

Actually, this isn't quite what I want. Assuming the means of $X$ and $Y$ are $\mu_x$ and $\mu_y$, I want to know $L(\mu_x>\mu_y)$ or $L(\mu_z>0)$, not $P(X>Y)$. (As I get more and more data, I know the means more exactly, so the likelihood should go to 0 or 1.) Also, is a prior needed?

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Indeed, otherwise $\mu_x$ and $\mu_y$ are just numbers hence the probability that $\mu_x\gt\mu_y$ is not relevant, being $1$ if $\mu_x\gt\mu_y$ and $0$ otherwise.