For questions about the total variational distance (which can be used as a measure of the distance between probability distributions). See e.g. the paper "On choosing and bounding probability metrics" (2002, by Alison L. Gibbs and Francis Edward Su) for more details about this and other measures.
Questions tagged [total-variational-distance]
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Why is KL divergence used so often in Machine Learning?
The KL Divergence is quite easy to compute in closed form for simple distributions -such as Gaussians- but has some not-very-nice properties. For example, it is not symmetrical (thus it is not a metric) and it does not respect the triangular…
Federico Taschin
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When should one prefer using Total Variational Divergence over KL divergence in RL
In RL, both the KL divergence (DKL) and Total variational divergence (DTV) are used to measure the distance between two policies. I'm most familiar with using DKL as an early stopping metric during policy updates to ensure the new policy doesn't…
mugoh
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CS 285 Prof Sergey Levine Lecture, Bounding Derivation for Reinforcement Learning (TRPO)
How can we derive the final result? I can understand the first line, but don't know how the absolute term in the summation is replaced with $2\epsilon…
shashack
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