Optimizing Shallow Neural Network Covariate Relation with Bayesian Inference-Based Markov Chain Monte Carlo Simulation

In this method, the Neural Network (NN) architecture is optimized using Markov Chain Monte Carlo (MCMC) simulation, combining machine learning and statistical approaches to overcome their respective limitations. While machine learning models require large datasets for training and are highly data-driven, traditional Bayesian inference-based MCMC simulations are primarily applied to regression equations. This method leverages the MCMC simulation’s ability to account for heterogeneity and reduce bias while utilizing the NN’s capacity to model complex relationships through its hidden layers and transfer functions.