Include input uncertainty in output estimation

[manna88aero]

At the moment the tool can estimate the uncertainty of the output given an input. But what happens when the input is also uncertain?
In many practical cases the uncertainty in the input could be as large as the uncertainty of the output.
Would it be possible to integrate input uncertainty to the tool?

[felipe-tobar]

Dear manna88aero, Sorru for the delay. The models you refer to are called GP-LVM (Gaussian process latent variable model). In its current state, MOGPTK has not implemented GPLVMs, hopefully we can do it in the future. Best, Felipe.

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On 20-10-2023, at 06:02, manna88aero ***@***.***> wrote: At the moment the tool can estimate the uncertainty of the output given an input. But what happens when the input is also uncertain? In many practical cases the uncertainty in the input could be as large as the uncertainty of the output. Would it be possible to integrate input uncertainty to the tool? — Reply to this email directly, view it on GitHub <#67>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACT3KG47RJPU7INDACAZUHTYAI42XAVCNFSM6AAAAAA6IWI7ECVHI2DSMVQWIX3LMV43ASLTON2WKOZRHE2TGOBVGIZDKNY>. You are receiving this because you are subscribed to this thread.

[tdewolff]

On a side note, you can set Y_err when calling mogptk.Data(...) (see https://games-uchile.github.io/mogptk/data.html#mogptk.data.Data) to pass the standard deviation of the input data. When using the Exact model (the default), these will be passed as data_variance to https://games-uchile.github.io/mogptk/gpr/model.html#mogptk.gpr.model.Exact. Hopefully that satisfies your need.