Updates

docs/dgbm.md

@@ -57,7 +57,7 @@ where $\mu_{\mathbf{x}} \in \mathbb{R}^{D}$ represents a vector of conditional m
 
 with the variances on the diagonal and the covariances on the off-diagonal, for $ i=1, \ldots, N $. Other examples include the Cholesky Decomposition or a Low-Rank Covariance approximation of the covariance matrix. For additional details and available distributions, see März (2022a).
 
-### Normalizing Flows
+## Normalizing Flows
 
 Although the GAMLSS framework offers considerable flexibility, parametric distributions may prove not flexible enough to provide a reasonable approximation for certain dataset, e.g., for multi-modal distributions. For such cases, it is preferable to relax the assumption of a parametric distribution and approximate the data non-parametrically. While there are several alternatives for learning conditional distributions, we propose to use Normalizing Flows for their ability to fit complex distributions with only a few parameters. 
 

mkdocs.yml

@@ -38,13 +38,8 @@ nav:
     - Multivariate Gaussian Regression (Low-Rank Approximation): examples/MVN (Low-Rank).ipynb
     - Multivariate Student-T Regression (Cholesky Decomposition): examples/MVT (Cholesky).ipynb
     - Zero-Adjusted Gamma Regression: examples/ZAGamma_simulation_example.ipynb
-#    - Zero-Inflated Negative Binomial Regression: examples/ZINB_example.ipynb
-#    - Zero-Inflated Poisson Regression: examples/ZIPoisson_example.ipynb
     - Gamma Regression (Boston Housing Data): examples/boston_housing_example_Gamma.ipynb
     - Expectile Regression: examples/simulation_example_Expectile.ipynb
     - Gaussian Regression: examples/simulation_example_Gaussian.ipynb
-#    - Negative Binomial Regression: examples/simulation_example_NegativeBinomial.ipynb
-#    - Poisson Regression: examples/simulation_example_Poisson.ipynb
     - Spline Flow Regression: examples/simulation_example_SplineFlow.ipynb
-#    - Student-T Regression: examples/simulation_example_StudentT.ipynb
   - API Docs: api.md