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StatMixedML · Jul 20, 2023 · 3d3479c · 3d3479c
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@@ -41,16 +41,12 @@ LightGBMLSS currently supports the following [PyTorch distributions](https://pyt
 | :----------------------------------------------------------------------------------------------------------------------------------: |:------------------------: |:-------------------------------------:     | :-----------------------------: | :-----------------------------: | 
 | [Beta](https://pytorch.org/docs/stable/distributions.html#beta)                                                                      | `Beta()`                  | Continuous <br /> (Univariate)             | $y \in (0, 1)$                  | 2                               |
 | [Cauchy](https://pytorch.org/docs/stable/distributions.html#cauchy)                                                                  | `Cauchy()`                | Continuous <br /> (Univariate)             | $y \in (-\infty,\infty)$        | 2                               |
-| [Dirichlet](https://pytorch.org/docs/stable/distributions.html#dirichlet)                                                            | `Dirichlet(D)`            | Continuous <br /> (Multivariate)           | $y_{D} \in (0, 1)$              | D                               |
 | [Expectile](https://epub.ub.uni-muenchen.de/31542/1/1471082x14561155.pdf)                                                            | `Expectile()`             | Continuous <br /> (Univariate)             | $y \in (-\infty,\infty)$        | Number of expectiles            |
 | [Gamma](https://pytorch.org/docs/stable/distributions.html#gamma)                                                                    | `Gamma()`                 | Continuous <br /> (Univariate)             | $y \in (0, \infty)$             | 2                               |
 | [Gaussian](https://pytorch.org/docs/stable/distributions.html#normal)                                                                | `Gaussian()`              | Continuous <br /> (Univariate)             | $y \in (-\infty,\infty)$        | 2                               |
 | [Gumbel](https://pytorch.org/docs/stable/distributions.html#gumbel)                                                                  | `Gumbel()`                | Continuous <br /> (Univariate)             | $y \in (-\infty,\infty)$        | 2                               |
 | [Laplace](https://pytorch.org/docs/stable/distributions.html#laplace)                                                                | `Laplace()`               | Continuous <br /> (Univariate)             | $y \in (-\infty,\infty)$        | 2                               |
 | [LogNormal](https://pytorch.org/docs/stable/distributions.html#lognormal)                                                            | `LogNormal()`             | Continuous <br /> (Univariate)             | $y \in (0,\infty)$              | 2                               |
-| [Multivariate Normal (Cholesky)](https://pytorch.org/docs/stable/distributions.html#multivariatenormal)                              | `MVN(D)`                  | Continuous <br /> (Multivariate)           | $y_{D} \in (-\infty,\infty)$    | D(D + 3)/2                      |
-| [Multivariate Normal (Low-Rank)](https://pytorch.org/docs/stable/distributions.html#lowrankmultivariatenormal)                       | `MVN_LoRa(D, rank)`       | Continuous <br /> (Multivariate)           | $y_{D} \in (-\infty,\infty)$    | D(2+rank)                       |
-| [Multivariate Student-T](https://docs.pyro.ai/en/stable/distributions.html#multivariatestudentt)                                     | `MVT(D)`                  | Continuous <br /> (Multivariate)           | $y_{D} \in (-\infty,\infty)$    | 1 + D(D + 3)/2                  |
 | [Negative Binomial](https://pytorch.org/docs/stable/distributions.html#negativebinomial)                                             | `NegativeBinomial()`      | Discrete Count <br /> (Univariate)         | $y \in (0, 1, 2, 3, \ldots)$    | 2                               |
 | [Spline Flow](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline)                                | `SplineFlow()`            | Continuous \& Discrete Count <br /> (Univariate)   | $y \in (-\infty,\infty)$ <br /> <br /> $y \in [0, \infty)$  <br  /> <br /> $y \in [0, 1]$  <br  />  <br /> $y \in (0, 1, 2, 3, \ldots)$ | 2xcount_bins + (count_bins-1) (order=quadratic)  <br  /> <br  />  3xcount_bins + (count_bins-1) (order=linear)                            |
 | [Poisson](https://pytorch.org/docs/stable/distributions.html#poisson)                                                                | `Poisson()`               | Discrete Count <br /> (Univariate)         | $y \in (0, 1, 2, 3, \ldots)$    | 1                               |