The Jacobian adjustment is one of those foundational concepts that trips up a lot of people working with probabilistic models, especially in variational inference and normalizing flows. This piece from Towards Data Science breaks down *why* you can't just transform random variables without accounting for how that transformation stretches or compresses probability mass. Worth bookmarking if you've ever gotten weird results from a change of variables.
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