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implemented predict_y and predict_noise #894

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@hstojic hstojic commented Jan 21, 2025

this is an important change, it will affect DE model quite a bit, so its important to test downstream consequences

  • predict method now outputs estimation uncertainty, which is more appropriate
  • DE now supports predict_y method which outputs combined uncertainty
  • there is a new method predict_noise that gives mean and variance of the noise (i.e. aleatoric uncertainty), which comes handy in some situations

note that trajectory sampler behaviour will be affected as well, but only in the diversify mode

aleatoric_var, _ = model.predict_noise(qp)
_, total_var = model.predict_y(qp)

assert tf.reduce_all(total_var >= epistemic_var)
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assert tf.reduce_all(total_var >= epistemic_var)
assert tf.reduce_all((total_var >= epistemic_var) & (total_var >= aleatoric_var))

Comment on lines +117 to 119
using a reparametrisation trick to speed up computation. Only epistemic uncertainty is taken
into account in sampling. Note that quantiles are not true trajectories, so this will likely
trajectories, so this will likely have some performance costs.
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Suggested change
using a reparametrisation trick to speed up computation. Only epistemic uncertainty is taken
into account in sampling. Note that quantiles are not true trajectories, so this will likely
trajectories, so this will likely have some performance costs.
using a reparametrisation trick to speed up computation. Only epistemic uncertainty is taken
into account in sampling. Note that quantiles are not true trajectories, so this will likely
have some performance costs.

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2 participants