Maximum likelihood estimate of data

Question

I have to find the maximum likelihood estimate of the parameter $\theta$ given the following data where $x$ is the observation from a discrete distribution and $f(x;\theta)$ is the probability mass function, $\theta\in\{1,2,3\}$. $$ \begin{matrix} x&f(x;1)&f(x;2)&f(x;3) \\ 0&1/3&1/4&0 \\ 1&1/3&1/4&0 \\ 2&0&1/4&1/4 \\ 3&1/6&1/4&1/2 \\ 4&1/6&0&1/4 \end{matrix} $$ I understand that for each observation, the maximum likelihood estimate is the value of $\theta$ that maximizes $f(x;\theta)$, but I don't understand how I can find the parameter value that maximizes the likelihood of the whole data.