Efficient Processing and Analysis of Nonlinear Random Equation Systems with Mixture Models in a Bayesian Framework

The core message of this article is to present a general framework for efficiently processing and analyzing nonlinear random equation systems by combining them with mixture model parameter random variables in a Bayesian framework. This allows investigating the combinatorial complexity of such equations and utilizing the insights for practical applications.