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I am learning about Deep Generative Models, tutorials all over the places use symbols and no one actually explaining the meaning of it. Can you please suggest the meaning of these terms?

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In statistics, I read the "twiddle" notation ($\sim$) as: "is distributed as". The RHS should be a probability model, or family of distributions thereof. We are left to gather than $p_{data}$ is an unobserved data generating mechanism. But by running some ML-magic, you can get a $p_{model}$ which is a probability model for simulating outcomes. "Want to learn p-model similar to p-data" is just bad English, but the idea is just goodness of fit. Observed should accord with expected.

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