We have likelihood function
$$
L\left(\mathbf{\Theta} \middle| x_1, \ldots, x_n \right) = \prod^n_{i=1} f\left( x_i \middle| \mathbf{\Theta} \right)
$$
and a score function
$$
V\left(\mathbf{\Theta} \middle| x_1, \ldots, x_n \right) = \nabla_\Theta \sum^n_{i=1} \ln{f\left(x_i, \mathbf{\Theta} \right)},
$$
where $x_i$ are observations and $\mathbf{\Theta}$ is the vector of parameters.
If we consider the maximum likelihood estimator $\mathbf{\Theta^{\ast}}$, it's the extreme of $L$, so
$$
V\left(\mathbf{\Theta^{\ast}}\right) = 0,
$$
but we need $L\left(\mathbf{\Theta^{\ast}}\middle| x_1, \ldots, x_n \right) \neq 0$.
So, is this inequality always true or is there a situation, where the likelihood with the MLE could be equal to zero?
Best Answer
If you have a very inadequate model such that at least one discretely (or continuously) distributed observation has zero probability (or probability density) for any parameter value $\theta\in\Theta$, that is, you essentially observe something that is impossible under that model, then yes, your maximum likelihood would be zero.