# Faster gp fitting on million observations?

**URL:** <https://discourse.julialang.org/t/faster-gp-fitting-on-million-observations/59519>\
**Category:** Probabilistic Programming\
**Tags:** gaussian-process\
**Created:** [April 18, 2021, 6:02am UTC](https://discourse.julialang.org/t/faster-gp-fitting-on-million-observations/59519 "2021-04-18T06:02:40Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![jzr](https://avatars.discourse-cdn.com/v4/letter/j/eb9ed0/32.png) [@jzr](https://discourse.julialang.org/u/jzr)\
**Post date:** [April 19, 2021, 10:44am UTC](https://discourse.julialang.org/t/faster-gp-fitting-on-million-observations/59519/11 "2021-04-19T10:44:46Z")

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> [@willtebbutt](#):
>
> Are you expecting quite a lot of variation through time in the quantity that you’re modelling?

Not too much, but some. To quantify a bit: There are about 300-1000 observations per day for some years. I think the overall standard deviation is 20x the standard deviation of the daily means. But during some periods there is a marked change in the value for a while, which I would like to capture.

> [@mschauer](#):
>
> Is the `U[0,1]` noise model here correct or should this be `randn` ? Then your live is easier.

`randn` is what I meant, right.

> [@theogf](#):
>
> Also, how many predictors do you have?

In addition to the temporal variable, there are 2-5 continuous predictors and another 2-5 categorical ones.

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