Streamed batch training with manual update - scaling considerations

[dustin]

@MushroomHunting

Is the scale parameter only relevant if one uses inference.run at first with N > M training data points.

To some degree, yes. It’s used generally to scale any computation with respect to the random variables. For example, you might use it for masking.

@MushroomHunting

Is n_samples only relevant if one uses an inference. run. call? I.e. if from the beginning if you have a large amount of data to train first on, and then the model is exposed to completely new data in a streaming situation. If not where does this fit in

n_samples is an algorithm hyperparameter in ed.KLqp, representing the number of samples to estimate the gradient of the loss function. It is always relevant whenever running the algorithm.

@MushroomHunting

In the case of having no data initially, and then acquiring data accumulating into buffers of size M, is it enough to simply have N=M giving a scale of 1 ?

That would only work if after each streaming batch, you re-set the prior distribution to be the inferred posterior from the batch. Otherwise, imagine you had a billion streaming points, with a batch size of 1; without scaling the likelihood by 1 million, the prior overwhelms the likelihood so the posterior will not differ much from the prior.

The discussion in Iterative estimators ("bayes filters") in Edward? - #2 by dustin provides most detail.