Comments (3)
If you change to the ForwardDiff backend it works:
https://github.com/TuringLang/Turing.jl/wiki/Get-started#changing-default-settings
> Turing.setadbackend(:forward_diff)
:forward_diff
> turing_result = turing_inference(prob, MethodOfSteps(RK4()), t_obs, data, [Gamma(1.0, 1.0)]; num_samples=500)
[Turing] looking for good initial eps...
[Turing.NUTS] found initial ϵ: 0.05
[NUTS] Sampling... 0% ETA: 2:00:49[Turing.WARNING]: Numerical error has been f[NUTS] Sampling... 23% ETA: 0:01:56
etc. We should try to get ReverseDiff working though. It works with the DiffEq solver though, so I'm not sure where it's running into an issue.
Pinging @xukai92
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With the newest Turing (master) this issue with Tracked variables seems to show up everywhere. @xukai92 can you take a look at our current code and let us know what areas are supposed to be differentiated? Since when the tracked variables hit the posterior distributions they give an error.
from diffeqbayes.jl.
This should work fine now, let us know if you still face issues with the latest release.
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Related Issues (20)
- Parallel abc_inference HOT 1
- TagBot trigger issue HOT 30
- abc_inference with parallel = true gives error HOT 2
- Change ABC inference engine? HOT 2
- DomainError: Starting point has non-finite density using DynamicHMC backend HOT 7
- Use updated `build_function` HOT 3
- Possible regression in Turing integration HOT 2
- Updates for StanSample.jl HOT 12
- Turing inference BoundsError when save_idxs is single value HOT 4
- Remove Requires.jl usage through subpackaging
- Dependency problem HOT 4
- Add a pointer to the Turing.jl direct usage tutorial from the documentation
- abc_inference is not included but exported HOT 1
- Default sigma priors differ for Stan and Turing
- Default dimensionality of Sigma differs for Stan and Turing
- Duplicate using statement and formatting
- Multiple shooting analogue for MCMC HOT 1
- need second order ode example HOT 2
- Support for sampling from multiple chains is missing from `turing_inference` HOT 2
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