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Nested Sampling is not a learning algorithm, it is a technique for numerical integration.


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Data is just a bunch of measurements. What constitutes the "ground truth" and "external labels" is determined by people. Take this data for example: First image is of a wild dog, then some measurements of its characteristics Second image is of a wild cat, then some measurements of its characteristics Third image is of a domesticated cat, then some ...


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It just means taking the mean of the data. You can do this by finding the mean of each marginal distribution and putting the marginal means into a vector that is the multivariate mean. Example: Let $X=(X_1, X_2, X_3)$ have $\bar{X}_1=3$, $\bar{X}_2=-13$, and $\bar{X}_3=-5$. The the multivariate mean is $\bar{X} = (3, -13, -5)$. (I think this assumes using $...


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Elbow method is a heuristic. There's no "mathematical" definition and you cannot create algorithm for it, because the point of the method is about visually finding the "breaking point" on the plot. This is subjective criteria and it often happens that different people could end up with different conclusions given same plots.


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