If you're considering a scenario where a neural network is used with a loss function to approximate a distribution, it's important to note that this approach may have limitations compared to traditional MCMC methods, especially if you require accurate and efficient sampling from the true underlying distribution. MCMC methods are specifically designed for sampling and exploring complex distributions, while neural networks are primarily designed for function approximation and optimization tasks.