Hi I am new to Julia and Turing and am trying to fit a forced 0-D box ODE to data, but I get type error when doing sampling. The tutorial ( Bayesian Estimation of Differential Equations ) gives an example for unforced LV model. Following this page ( julia - solve system of ODEs with read in external …

[image] Duo_Chan: predicted = solve(ode_lin, Tsit5(); p=p, saveat=1) You need to do u0 = typeof(α).(ode_lin.u0) to promote it to dual. But I have an idea for how to automate this promotion, so I’ll instead respond with a PR in a second.

I tried to run your code, but I received the following error: ERROR: UndefVarError: g1_lin not defined Stacktrace: [1] top-level scope @ Untitled-2:47 I might be able to provide more guidance if given g1_lin or the full error message. Automatic differentiation uses the Dual type to compute gr…

Hi Christopher, Thanks for your quick response. the g1_lin was a bug, which I have fixed by replacing them with F, which stands for forcing. That said, I still get the same error message regarding type errors. The error message is quite long, copy the entire message goes beyond the length limit,…

[image] Duo_Chan: If interpolation is the problem, is there any way to walk around? Yes, just do what I mentioned. Though there’s a fix coming soon as well.

Yes, that solves the problem! Thanks!

Awesome, so then this PR will do that automatically behind the scenes in the future: https://github.com/SciML/DiffEqBase.jl/pull/773 That should be released in a few hours, but it’s just a helper to do the same thing.

TypeError in Julia/Turing when sampling for a forced differential equation

General Usage

ChrisRackauckas June 18, 2022, 4:16pm 5

Yes, just do what I mentioned. Though there’s a fix coming soon as well.

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TypeError in Julia/Turing when sampling for a forced differential equation

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