Implement ordinal stopping-ratio distribution#963
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Formula from the Handbook of Differential Entropy, J.V. Michalowicsz, J.M. Nichols, F. Bucholtz p. 113, 114
Co-Authored-By: Srinivas Vasudevan <srvasude@google.com>
Co-authored-by: Srinivas Vasudevan <srvasude@google.com>
Co-authored-by: Srinivas Vasudevan <srvasude@google.com>
Co-authored-by: Srinivas Vasudevan <srvasude@google.com>
…n mean and variance
Replace scale and concentration by the underlying logistic parameters : loc and scale. !!!WARNING: some numpy tests fail for an unknown reason and still need to be fixed!!!
Co-authored-by: Srinivas Vasudevan <srvasude@google.com>
Co-authored-by: Srinivas Vasudevan <srvasude@google.com>
Optimize import and fix names in _params_event_ndims
… at present (computed Boolean masks). Add jax_omit_deps to allow this. PiperOrigin-RevId: 318536818
END_PUBLIC *** Original change description *** BEGIN_PUBLIC Disable hidden_markov_model_test on JAX. PiperOrigin-RevId: 318537913
For rewrite.py, this meant just adding more runtime checks to convince PyType that we're doing everything properly. The final code certainly has better errors. For fun_mcmc_lib.py, there's nothing we can do as nests cannot be described precisely by the type system, so we switch to regular casts. PiperOrigin-RevId: 318546801
…interaction not yet well supported by tfp.jax. PiperOrigin-RevId: 318885402
PiperOrigin-RevId: 318885822
Also revert horseshoe_test now that JAX uses a more numerically stable log1p. PiperOrigin-RevId: 318899551
PiperOrigin-RevId: 318904656
….TransformedDistribution` in tfp.sts. PiperOrigin-RevId: 318922723
PiperOrigin-RevId: 318924401
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I think I messed up to rebase the latest commits from master into my branch. I guess it makes more sense to open another PR and close this once we finished the reviews? |
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Sorry, forgot to ping @srvasude . |
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This will be impossible to review until we sort out the base (it's showing changes in 361 files). Feel free to send another PR and close this if it's easier for you. |
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Yeah, you're right, sorry. I'll close this and open up another one. |
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Hello, this PR again implements the stopping ratio logistic distribution which was already discussed and reviewed [here](#963). @srvasude , sorry for closing the other PR. I worked in your requested changes other than the transpose that needs to be done when sampling. Thanks. Cheers, Simon COPYBARA_INTEGRATE_REVIEW=#990 from dirmeier:stopping_ratio 0743b54 PiperOrigin-RevId: 346376187
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Hello, this PR again implements the stopping ratio logistic distribution which was already discussed and reviewed [here](tensorflow#963). @srvasude , sorry for closing the other PR. I worked in your requested changes other than the transpose that needs to be done when sampling. Thanks. Cheers, Simon COPYBARA_INTEGRATE_REVIEW=tensorflow#990 from dirmeier:stopping_ratio 0743b54 PiperOrigin-RevId: 346376187
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Hello,
this PR implements the sequential stopping-ratio distribution, an ordinal distribution where categories can only be reached one after another. Solves this issue.
Cheers,
Simon