What makes sequential Bayesian updating more challenging compared to full batch updating?

I think GFlowNets can help with sequential updating without the limitations of variational inference (which is, I guess, what @ricardoV94 meant by the phrase “textbooks always work with simple conjugate prior models”): see section 4. in Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.. This is what I suggested to implement in Sampling with a diffusion model

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