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In high dimensions, storing all of the samples to fit a model can require a lot of RAM. This could be avoiding by switching to an online learning algorithm for model fitting.
The text was updated successfully, but these errors were encountered:
Further to this, this could be linked to using NF to draw more samples from the posterior for fitting rather than train/test chain split - will discuss this later after more pressing releases
In high dimensions, storing all of the samples to fit a model can require a lot of RAM. This could be avoiding by switching to an online learning algorithm for model fitting.
The text was updated successfully, but these errors were encountered: