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Just curious to know the meaning contained in the latent variables used in the machine learning applications ?

Basically, I am confused if latent variables present the same essence as the model parameters ? In Parameters vs latent variables, @alberto suggested that parameters are fixed while latent variables are essentially random variables that are not observed directly.

My point is that even a parameter is a random variable (its just that we do not know the value of this parameter). Surely, we know the distribution that it is coming from and using this distribution as a prior information, we try to estimate the value that fits the training data well. Am I going well with this concept ?

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    $\begingroup$ Parameters are constant across all data points while latent variables can vary. Yes, parameters can be treated as random variables too, which was pointed out in the other thread. $\endgroup$
    – shimao
    Commented Mar 16, 2018 at 4:16
  • $\begingroup$ @shimao I've copied your comment as an answer. If you would prefer to write your own answer, please let me know and I'll delete mine. $\endgroup$
    – Sycorax ♦
    Commented Jul 28, 2018 at 16:40

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Parameters are constant across all data points while latent variables can vary. Yes, parameters can be treated as random variables too, which was pointed out in the other thread.

I've copied @shimao's comment as an answer because the comment is, more or less, an answer to this question. We have a dramatic gap between answers and questions. At least part of the problem is that some questions are answered in comments: if comments which answered the question were answers instead, we would have fewer unanswered questions.

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