# What is the correct way to refer to the Likelihood $L(\theta, \alpha | X)$ where $\theta$ and $\alpha$ are parameters?

I am currently studying some latent variable models.

In many works, I found the following equation:

$$L( \theta, \boldsymbol\pi | x ) = \sum_{c=1}^{C} f(x| \theta, \alpha = \alpha_c) \cdot \pi_c$$

where $$\theta$$ is a continuous parameter, $$\alpha$$ is latent variable (categorical, in the case), and $$\boldsymbol\pi = (\pi_1, \ldots, \pi_C)$$ where $$\pi_c = P(\alpha = \alpha_c)$$.

In those works, $$L( \theta, \boldsymbol\pi | x )$$ is usually called of "marginalized likelihood" or "marginal likelihood".

My question is how can I refer to the likelihood function BEFORE the marginalization, that is, $$L(\theta, \alpha |x) = f(x | \theta, \alpha)$$ ?

I intend to mention both functions, so I think calling the latter only "likelihood" would be a bit vague.

Can someone tell me what is the most correct term to refer to the likelihood $$L(\theta, \alpha |x)$$?

$$p(x|\pi)=\sum_{c} f(x|\theta, \alpha=\alpha_c).\pi_c$$
• Thanks for your answer,I have two comments. 1) In the example of the first link, the marginal likelihood depends only of the data, which is not my case. 2) The example of the Wikipedia are, indeed, equal to mine; but please notice that my question is not about the marginal likelihood, but about the "unmarginalized" likelihood $f(x | \theta, \alpha)$. Commented Oct 26, 2023 at 23:00