Flow-based generative model: Difference between revisions

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Simplex calibration transform: added reference to SGB distribution
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R^\Delta_\text{cal}(\mathbf p; a, \mathbf c) = \left|\operatorname{det}(\mathbf{RF_pE})\right| = a^{1-n}\prod_{i=1}^n\frac{q_i}{p_i}
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* This result can also be obtained by factoring the density of the [[SGB distribution]],<ref name="sgb">{{cite web |last1=Graf |first1=Monique (2019)|title=The Simplicial Generalized Beta distribution - R-package SGB and applications |url=https://libra.unine.ch/server/api/core/bitstreams/dd593778-b1fd-4856-855b-7b21e005ee77/content |website=Libra |access-date=26 May 2025}}</ref> which is obtained by sending [[Dirichlet distribution|Dirichlet]] variates through <math>f_\text{cal}</math>.
While calibration transforms are most often trained as [[discriminative model|discriminative models]], the reinterpretation here as a probabilistic flow allows also the design of [[generative model|generative]] calibration models based on this transform.