Comments (3)
As the debug log will confirm, you have a likelihood plateau, with multiple likelihood points having the same value. To support plateaus within nested sampling, the live points need to be reduced. If all live points have the same value, the problem seems trivially solved (uninformative likelihood, prior=posterior).
I would suggest you modify your likelihood so that you do not return just -1e300, but introduce a slight slope which increases towards the physical space. For example, in the else
clause, set log_like = -1e300 * np.abs(val - 0.6)
Even better would be if you defined your prior so that that unphysical space cannot be proposed.
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To your last question, this is independent of the proposal.
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Thank you @JohannesBuchner! I will try both approaches!
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Related Issues (20)
- store dimensionality in points file HOT 1
- refine slow warning
- Ultranest 3.6.1 does not install in new .venv environment HOT 14
- Difficulty installing Ultranest versions prior to 3.6.2 HOT 4
- Feature request/bug: returning float32 from log-likelihood fn with PopulationSliceSampler HOT 7
- MPI fails with likelihoods that have plateaus HOT 4
- ValueError: Buffer has wrong number of dimensions (expected 2, got 0) HOT 1
- logl values associated with equally weighted samples HOT 4
- Conda installer for osx-arm64 HOT 3
- Example of derived_param_names HOT 5
- Feature request: HDI intervals HOT 6
- Unexpected results for PredictionBand HOT 5
- Unexpected results for PredictionBand HOT 1
- Eggbox problem intractable for Ultranest HOT 3
- Ultranest hangs upon completion HOT 1
- Pass parameters to loglike and prior transform HOT 2
- [Docs] Outdated tutorial example HOT 8
- [Question] Popstepsampler: Number of walkers and live points HOT 12
- 'OSError: [Errno 121]' while running ultranest HOT 3
- Store version in points hdf5 file HOT 1
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