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Signed-off-by: Nathaniel <[email protected]>
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docs/source/notebooks/iv_vs_priors.ipynb

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"So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
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"This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
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"### The Regularised Horseshoe: Continuos Moderation\n",
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"### The Regularised Horseshoe: Gentle Moderation\n",
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"The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
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