The uncertainty estimation provided by the Bayesian framework can be further improved by incorporating more flexible approximation techniques like normalizing flows or Stein Variational Gradient Descent. Normalizing flows allow for more complex posterior distributions to be approximated, enabling a more accurate representation of uncertainty in the predictions. By leveraging the expressiveness of normalizing flows, the Bayesian framework can capture intricate dependencies in the data and provide more nuanced uncertainty estimates. Similarly, Stein Variational Gradient Descent offers a computationally efficient way to approximate complex posterior distributions, enhancing the uncertainty quantification capabilities of the Bayesian framework. By integrating these advanced approximation techniques, the Bayesian approach can offer more refined and reliable uncertainty estimates in a wide range of applications.