Regression Models#
The regression models follow the same conventions as the estimation models (see Estimation Models). Select a model by string name in MMDRegressor (e.g. model="linear-gaussian-loc") or pass a class instance directly. Click a model name for its full reference page.
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Linear regression \(Y \mid X \sim \mathcal{N}(X^\top\beta, \phi)\) with both regression coefficients \(\beta\) and noise variance \(\phi\) estimated jointly. |
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Linear regression \(Y \mid X \sim \mathcal{N}(X^\top\beta, \phi)\) with regression coefficients \(\beta\) estimated and noise variance \(\phi\) fixed. |
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Logistic regression \(Y \mid X \sim \mathrm{Bernoulli}(\sigma(X^\top\beta))\) with regression coefficients estimated. |
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Gamma regression with mean \(\mu = \exp(X^\top\beta)\); coefficients \(\beta\) and shape parameter estimated jointly. |
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Gamma regression with mean \(\mu = \exp(X^\top\beta)\); coefficients \(\beta\) estimated and shape parameter fixed. |
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Poisson regression \(Y \mid X \sim \mathrm{Poisson}(\exp(X^\top\beta))\) with regression coefficients estimated. |
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Beta regression where both |
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Beta regression with the precision |