This is more a discussion point than an "issue".
Myself and @seabbs are really interested in using AutoGP inside of a larger project.
Rough concept
The rough concept would be to use AutoGP to power inference on the time-varying reproductive number of an infection process that we observe via eventual determined cases:
$$\begin{aligned}
\log R_t &\sim GP_t \\\
I_t &= R_t (I \circ g)_t \\\
y_t &\sim \text{ObsKernel}(I_{t-1}, I_{t-2},...)
\end{aligned}$$
where $R_t$ is the time-varying reproduction number, $I_t$ are the daily actual infections which depend on $R_t$ and the past infections smoothed by convolution with a vector g (aka the generation distribution), $y_t$ are the actual observations which depend on past infections via some observation kernel ObsKernel.
Feasibility of using AutoGP
The idea would be use AutoGP functionality, e.g. proposing/accepting-rejection of GP kernel compositions, inside a model structured as above.
Does anyone have a sense of how feasible that would be: my first past thought are not to fork AutoGP but rather to doing using and then pull out the bits of AutoGP under-the-hood code we'd want. We'd be declaring the probabilistic model described above in the Gen.jl PPL.
This is more a discussion point than an "issue".
Myself and @seabbs are really interested in using
AutoGPinside of a larger project.Rough concept
The rough concept would be to use
AutoGPto power inference on the time-varying reproductive number of an infection process that we observe via eventual determined cases:where$R_t$ is the time-varying reproduction number, $I_t$ are the daily actual infections which depend on $R_t$ and the past infections smoothed by convolution with a vector $y_t$ are the actual observations which depend on past infections via some observation kernel
g(aka the generation distribution),ObsKernel.Feasibility of using
AutoGPThe idea would be use
AutoGPfunctionality, e.g. proposing/accepting-rejection of GP kernel compositions, inside a model structured as above.Does anyone have a sense of how feasible that would be: my first past thought are not to fork
AutoGPbut rather to doingusingand then pull out the bits ofAutoGPunder-the-hood code we'd want. We'd be declaring the probabilistic model described above in theGen.jlPPL.