Comments (4)
In addition to these features, let's also add a verbose flag to control the intermediate sampling info (like [PG]: Iter ...), which can lead to clear test log by turning it off in tests.
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I adapted the code from Mamba.jl
for MCSE and ESS (#74). Do you have a reference for IAT in detail? (or maybe I don't really need it as we already have ess).
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I've merged this PR.
We don't really need IAT for now - I have a plan to use Mamba's Chain
type to store Turing's MCMC output. It will allow us to share summary statistics functions and plotting utilities from the Mamba package systematically. We can talk more about this later and maybe implement it in the next release.
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Closed by PR #90
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Related Issues (20)
- Implementing `adtype` for closely linked "external" samplers / adding metric warm-up HOT 2
- Constrained or partitioned inference for Turing models.
- Compat issue HOT 13
- missing keyword arguments not property processed HOT 5
- Enable Aqua.test_ambiguities
- Progress reporting in parallel sampling HOT 3
- Using @distributed to accelerate but getting wrong results
- Mode estimation's support of Pathfinder integration HOT 5
- Docs on SGHMC / SGLD? HOT 4
- Failing in precompiling dependencies HOT 2
- AbstractMCMC docs page out of date HOT 1
- Attempt fixing `bnn` tests in Ezyme PR HOT 3
- Make MLE and MAP more robust HOT 6
- Depreciate MCMC-interface code in favour of `AbstractMCMC`.
- Fix code test coverage HOT 1
- preallocating an array of random variables, getting `varname As used multiple times in model (subsumes As[1])` HOT 1
- maximum_likelihood function not found in Turing.jl HOT 4
- Allow for a named tuple for `initial_params` HOT 13
- Using the score function estimator as gradient for VI HOT 9
- Import style and modularisation HOT 9
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