Hello there, I’m new on probabilistic programming, and I’ve followed the Deep Markov Model tutorial (https://github.com/uber/pyro/tree/dev/examples/dmm), and I’m now testing the model on a custom dataset. The dataset I’m using is The “Appliances Energy Prediction Data Set”, uploaded on UCI Machine Learning Repository here.

What I’m trying to do here is to predict/estimate the “Appliances Energy Use” after training the model on the dataset, but I’m at loss on what I’m supposed to do. I’ve put all the variables on the dataset, including the aforementioned “Appliances Energy Use” (except time) as the state variables/observations (**x**) during training, but I don’t know how to test the trained model on a sequence of observations, with the variable I want to predict (“Appliances Energy Use” in this case) ungiven (In other words, I’m trying to infer a missing variable given a set of observations).

What I want to ask is:

- Can DMM do this? As I’m new to the workings of this model and probabilistic programming, I might have misunderstood the model and thought this kind of inference is possible.
- If it can, what is the steps I need to take to do this? I’m thinking of using all the variables, minus the missing variable, as the input of guide, and using the inferred latent variables as input to the model to emit the whole observations (as it includes the missing variable, this means predicting the variable too).

I’m using Pyro v0.3.0 and Pytorch v1.0. Any help is appreciated!