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[Question] Streaming Gaussian process regression

See original GitHub issue

I want to do Gaussian process regression on a data stream where Gaussian process should be updated progressively and new predictions are made when new data comes. I am aware of https://github.com/cornellius-gp/gpytorch/issues/1784. If I understand correctly, get_fantasy_model and set_train_data do different things.

get_fantasy_model merges the provided input samples with existing samples
set_train_data use the provided input samples to replace existing samples

For streaming Gaussian process regression, get_fantasy_model should be used. However, I wonder if the prediction performance will degrade when number of samples increases.

Issue Analytics

State:
Created 2 years ago
Comments:5

Top GitHub Comments

1reaction

wjmaddoxcommented, Nov 15, 2021

Btw, this is implemented natively inside of GPyTorch now as part of the get_fantasy_model function for traditional SKI models.

0reactions

metab0tcommented, Nov 15, 2021

I have seen your repo and paper: https://github.com/wjmaddox/online_gp I will spend some time reading the paper. Thank you!

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