Tensorflow-probability

Latest version: v0.24.0

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0.18.0

Release notes

This is the 0.18.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.10 and JAX 0.3.17 .


Change notes

[coming soon]


Huge thanks to all the contributors to this release!

[coming soon]

0.17.0

Release notes

This is the 0.17.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.9.1 and JAX 0.3.13 .


Change notes

* Distributions
- Discrete distributions transform correctly when a bijector is applied.
- Fix bug in Taylor approximation of log-normalizing constant for the
`ContinuousBernoulli`.
- Add `TwoPieceNormal` distribution and reparameterize it's samples.
- Make `IncrementLogProb` a proper tfd.Distribution.
- Add quantiles to `Empirical` distribution.
- Add `tfp.experimental.distributions.MultiTaskGaussianProcessRegressionModel`
- Improve efficiency of `MultiTaskGaussian` Processes in the presence of
observation noise: Reduce complexity from O((NT)^3) to O(N^3 + T^3) where N
is the number of data points and T is the number of tasks.
- Improve efficiency of `VariationalGaussianProcess`.
- Add `tfd.LognNormal.experimental_from_mean_variance`.

* Bijectors
- Fix Softfloor bijector to act as the identity at high temperature, and floor
at low temperature.
- Remove `tfb.Ordered` bijector and `finite_nondiscrete` flags in Distributions.

* Math
- Add tfp.math.betainc and gradients with respect to all parameters.

* STS
- Several bug fixes and performance improvements to
`tfp.experimental.sts_gibbs` for Gibbs sampling Bayesian structural time
series models with sparse linear regression.
- Enable `tfp.experimental.sts_gibbs` under JAX

* Experimental
- Ensemble Kalman filter is now efficient in the case of ensemble size << observation size and an "easy to invert" modeled observation covariance.
- Add a `perturbed_observations` option to
`ensemble_kalman_filter_log_marginal_likelihood`.
- Add Experimental support for custom JAX PRNGs.

* Other

- Add `assertAllMeansClose` to `tfp.TestCase` for testing sampling code.

Huge thanks to all the contributors to this release!

- Adam Sorrenti
- Alexey Radul
- Christopher Suter
- Colin Carroll
- Du Phan
- Emily Fertig
- Fabien Hertschuh
- Faizan Muhammad
- Francois Chollet
- Ian Langmore
- Jacob Burnim
- Jake VanderPlas
- Kathy Wu
- Kristian Hartikainen
- Kyle Loveless
- Leandro Campos
- Xinle Sheila Liu
- ltsaprounis
- Matt Hoffman
- Manas Mohanty
- Max Jiang
- Pavel Sountsov
- Peter Hawkins
- Praveen Narayan
- Renu Patel
- Ryan Russell
- Scott Zhu
- Sergey Lebedev
- Sharad Vikram
- Srinivas Vasudevan
- tagoma
- Urs Koster
- Vaidotas Simkus
- Vishnuvardhan Janapati
- Yilei Yang

0.16.0

Release notes

This is the 0.16.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.8.0 and JAX 0.3.0 .


Change notes

[coming soon]


Huge thanks to all the contributors to this release!

- Alexey Radul
- Ben Lee
- Billy Lamberta
- Brian Patton
- Chansoo Lee
- Christopher Suter
- Colin Carroll
- Dave Moore
- Du Phan
- Emily Fertig
- François Chollet
- Gianluigi Silvestri
- Jacob Burnim
- Jake Taylor
- Junpeng Lao
- Matthew Johnson
- Michael Weiss
- Pavel Sountsov
- Peter Hawkins
- Rebecca Chen
- Sharad Vikram
- Soo Sung
- Srinivas Vasudevan
- Urs Köster

0.15.0

Release notes

This is the 0.15 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.7.0.

Change notes

- Distributions
- Add `tfd.StudentTProcessRegressionModel`.
- Distributions' statistics now all have batch shape matching the Distribution itself.
- `JointDistributionCoroutine` no longer requires `Root` when `sample_shape==()`.
- Support `sample_distributions` from autobatched joint distributions.
- Expose `mask` argument to support missing observations in HMM log probs.
- `BetaBinomial.log_prob` is more accurate when all trials succeed.
- Support broadcast batch shapes in `MixtureSameFamily`.
- Add `cholesky_fn` argument to `GaussianProcess`, `GaussianProcessRegressionModel`, and `SchurComplement`.
- Add staticmethod for precomputing GPRM for more efficient inference in TensorFlow.
- Add `GaussianProcess.posterior_predictive`.

- Bijectors
- Bijectors parameterized by distinct `tf.Variable`s no longer register as `==`.
- BREAKING CHANGE: Remove deprecated `AffineScalar` bijector. Please use `tfb.Shift(shift)(tfb.Scale(scale))` instead.
- BREAKING CHANGE: Remove deprecated `Affine` and `AffineLinearOperator` bijectors.

- PSD kernels
- Add `tfp.math.psd_kernels.ChangePoint`.
- Add slicing support for `PositiveSemidefiniteKernel`.
- Add `inverse_length_scale` parameter to kernels.
- Add `parameter_properties` to PSDKernel along with automated batch shape inference.

- VI
- Add support for importance-weighted variational objectives.
- Support arbitrary distribution types in `tfp.experimental.vi.build_factored_surrogate_posterior`.

- STS
- Support `+` syntax for summing `StructuralTimeSeries` models.

- Math
- Enable JAX/NumPy backends for `tfp.math.ode`.
- Allow returning auxiliary information from `tfp.math.value_and_gradient`.

- Experimental
- Speedup to `experimental.mcmc` windowed samplers.
- Support unbiased gradients through particle filtering via stop-gradient resampling.
- `ensemble_kalman_filter_log_marginal_likelihood` (log evidence) computation added to `tfe.sequential`.
- Add experimental joint-distribution layers library.
- Delete `tfp.experimental.distributions.JointDensityCoroutine`.
- Add experimental special functions for high-precision computation on a TPU.
- Add custom log-prob ratio for `IncrementLogProb`.
- Use `foldl` in `no_pivot_ldl` instead of `while_loop`.

- Other
- TFP should now support numpy 1.20+.
- BREAKING CHANGE: Stock unpacking seeds when splitting in JAX.

Huge thanks to all the contributors to this release!

- 8bitmp3
- adriencorenflos
- Alexey Radul
- Allen Lavoie
- Ben Lee
- Billy Lamberta
- Brian Patton
- Christopher Suter
- Colin Carroll
- Dave Moore
- Du Phan
- Emily Fertig
- Faizan Muhammad
- George Necula
- George Tucker
- Grace Luo
- Ian Langmore
- Jacob Burnim
- Jake VanderPlas
- Jeremiah Liu
- Junpeng Lao
- Kaan
- Luke Wood
- Max Jiang
- Mihai Maruseac
- Neil Girdhar
- Paul Chiang
- Pavel Izmailov
- Pavel Sountsov
- Peter Hawkins
- Rebecca Chen
- Richard Song
- Rif A. Saurous
- Ron Shapiro
- Roy Frostig
- Sharad Vikram
- Srinivas Vasudevan
- Tomohiro Endo
- Urs K&ouml;ster
- William C Grisaitis
- Yilei Yang

0.14.1

Release notes

This is the 0.14.1 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.6.0 and JAX 0.2.21.


Change notes

[coming soon]


Huge thanks to all the contributors to this release!

- 8bitmp3
- adriencorenflos
- allenl
- axch
- bjp
- blamb
- csuter
- colcarroll
- davmre
- derifatives
- emilyaf
- europeanplaice
- Frightera
- fmuham
- gcluo
- GianluigiSilvestri
- gisilvs
- gjt
- grisaitis
- harahu
- jburnim
- langmore
- leben
- lukewood
- mihaimaruseac
- NeilGirdhar
- phandu
- phawkins
- rechen
- ronshapiro
- scottzhu
- sharadmv
- siege
- srvasude
- ursk
- vanderplas
- xingyousong
- yileiyang

0.14.0

Release notes

This is the 0.14 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.6.0 and JAX 0.2.20.


Change notes

Please see the release notes for TFP 0.14.1 at https://github.com/tensorflow/probability/releases/v0.14.1 .

Huge thanks to all the contributors to this release!

- 8bitmp3
- adriencorenflos
- allenl
- axch
- bjp
- blamb
- csuter
- colcarroll
- davmre
- derifatives
- emilyaf
- europeanplaice
- Frightera
- fmuham
- gcluo
- GianluigiSilvestri
- gisilvs
- gjt
- grisaitis
- harahu
- jburnim
- langmore
- leben
- lukewood
- mihaimaruseac
- NeilGirdhar
- phandu
- phawkins
- rechen
- ronshapiro
- scottzhu
- sharadmv
- siege
- srvasude
- ursk
- vanderplas
- xingyousong
- yileiyang

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