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One more thing: I'd add computing and tracking two variances of the log probs:
variance over samples of logprobs_estimates (to understand better the behaviour of the correlation coefficient over the training)
median over samples of the variances of the logprobs_estimates over trajectories for each sample (to get a sense of how noisy the estimation is). The math is a bit tricky here as we use log mean as an estimation, not just the mean. But there're some work around: https://stats.stackexchange.com/questions/418313/variance-of-x-and-variance-of-logx-how-to-relate-them
But in any case, we will need to compute empirical var(P_F(tau) / P_B (tau)) / n_traj for each sample and then play around a bit with it to get variance for the log mean estimation.
One more thing: I'd add computing and tracking two variances of the log probs:
But in any case, we will need to compute empirical var(P_F(tau) / P_B (tau)) / n_traj for each sample and then play around a bit with it to get variance for the log mean estimation.
Originally posted by @AlexandraVolokhova in #167 (comment)
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