The inherent stochastic nature of neural network training, influenced by random initialization of parameters and convergence to diverse local optima, results in variations in the weights and biases across the ensemble models. Consequently, this diversity allows the ensemble method to effectively capture and quantify epistemic uncertainty, addressing the intrinsic limitations of single-model predictions by providing a more comprehensive uncertainty estimate. Ensemble was first introduced and discussed to improve the accuracy and diversity of a prediction. And later ensemble is found to be well-suited for UQ of neural networks. But it comes along with significantly increased computational effort and memory. 56