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Equation 4 · Part 2 · Training on Your Own Output: Synthetic Data and What It Does to a Distribution

Symbol F

p^k+1=F(X1,…,Xn∼p^k),\hat{p}_{k+1} = \mathcal{F}\left( X_1, \ldots, X_n \sim \hat{p}_k \right),
F\mathcal{F}

What this part means

the fitting procedure.

Its job in the formula

F is an input to the expression that computes the quantity on the left.

Where the article explains it

Let p^k\hat{p}_k be the model fitted at generation k , and F\mathcal{F} the fitting procedure.

The passage around this formula

Let p^k\hat{p}_k be the model fitted at generation k , and F\mathcal{F} the fitting procedure. The classical collapse setting is p^k+1=F(X1,…,Xn∼p^k)\hat{p}_{k+1} = \mathcal{F}\left( X_1, \ldots, X_n \sim \hat{p}_k \right). in which generation k+1 sees n samples drawn from its immediate predecessor and nothing else . The original data are gone. No filter selects among the samples. The lineage is single. Under those conditions, degradation compounds because there is no channel by which an error introduced at generation k can ever be corrected.

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Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

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Sources cited in the article section

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