A Bagging and Boosting Based Convexly Combined Optimum Mixture Probabilistic Model

TL;DR

This paper introduces a novel probabilistic model that combines bagging and boosting techniques to iteratively optimize mixture models for maximum p value, enhancing model accuracy.

Contribution

It proposes a new convex combination approach for mixture models using bagging and boosting, which was not explored in prior mixture distribution studies.

Findings

The model achieves higher p values compared to traditional methods.

Iterative optimization improves mixture model performance.

The approach effectively combines ensemble techniques with probabilistic modeling.

Abstract

Unlike previous studies on mixture distributions, a bagging and boosting based convexly combined mixture probabilistic model has been suggested. This model is a result of iteratively searching for obtaining the optimum probabilistic model that provides the maximum p value.