In contrast to PLSA, LDA further makes the assumption that these mixture distributions are Dirichlet-distributed random variables whose parameters have to be estimated from the training data.
The statistical method used here assumes that underlying mixture distributions are normally distributed and does not rule out the possibility that the two phenotypes are skewed or represent two separate peaks on a single underlying distribution.
More details about Mixture distributions can be found in [21].
Finite mixture distributions arise naturally in many applications including clustering and inference in heterogeneous populations.
Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity.
One can model a given response variable with a variety of mixture distributions.
For this reason, mixture distributions will be considered for GARX in the future.
Mixture distributions can model extreme events better than the basic Gaussian ones.
A common approach for fitting such a bimodal data is by using mixture distributions.
Though the superiority of proposed mixture functions in Station 46014 is not very significant, the proposed mixture distributions GEVL for Station 42056 and WGEV for Station 46012 have provided better fit of the empirical data than other existing mixture distributions.
In this study, we address this problem by introducing novel bivariate moment-closure methods based on mixture distributions.
