Explain why you would choose the most general theoretical guarantees for energy-based models over the more broadly applicable but case-by-case basis proofs required for other probability distributions in this specific GAN implementation. Discuss tradeoffs and implications for your architecture.

In this scenario, I would rely on the most general theoretical guarantees that naturally apply to all energy-based models because they provide a foundation for understanding the behavior of my MCMC methods without requiring extensive case-by-case analysis. This allows me to focus on other aspects of model design and optimization while leveraging well-established results from the field.