diff --git a/docs/dgbm.md b/docs/dgbm.md index 57c0f708..fa151ef6 100644 --- a/docs/dgbm.md +++ b/docs/dgbm.md @@ -42,7 +42,7 @@ Within the original distributional regression framework, the functions $f_{k}(\c ## Mixture Distributions -Mixture densities or mixture distributions offer an extension to the notion of traditional univariate distributions by allowing the observed data to be thought of as arising from multiple underlying processes. In its essence, a mixture distribution is a weighted combination of several component distributions, where each component contributes to the overall mixture distribution, with the weights indicating the importance of each component. For instance, if you imagine the observed data distribution having multiple modes, a mixture of Gaussians could be employed to capture each mode with a separate Gaussian distribution. +Mixture densities, or mixture distributions, offer an extension to the notion of traditional univariate distributions by allowing the observed data to be thought of as arising from multiple underlying processes. In its essence, a mixture distribution is a weighted combination of several component distributions, where each component contributes to the overall mixture distribution, with the weights indicating the importance of each component. For instance, if you imagine the observed data distribution having multiple modes, a mixture of Gaussians could be employed to capture each mode with a separate Gaussian distribution.