Timeline for How to make predictions with the posterior predictive distribution?
Current License: CC BY-SA 3.0
3 events
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| Jun 20, 2017 at 14:27 | comment | added | Stephan Kolassa | That's the way I would go about it. (Assuming that the mean or expectation is the one-number summary you are interested in.) | |
| Jun 20, 2017 at 9:21 | comment | added | hendiadys | So assuming that I want to make point predictions, I would do the following: 1) Select a number of rows, where I set the target variable $t$ to be missing, for these missing variables, the model will make an estimation. These observations are our so called test set. 2) Run the generative model via variational inference to estimate latent parameters $z_n$, regression parameters for $a$ and $b$ in the Weibull distribution. 3) Calculate the mean of the density for the observations where $t$ is missing, and take them as my point predictions. Would this procedure make any sense? | |
| Jun 16, 2017 at 9:52 | history | answered | Stephan Kolassa | CC BY-SA 3.0 |