Tensorflow-probability

Release notes

This is the 0.19.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.11 and JAX 0.3.25 .


Change notes


* Bijectors
- Added `UnitVector` bijector to map to the unit sphere.


* Distributions
- Added noncentral Chi2 distribution to TFP.
- Added differentiable quantile and cdf function approximation to NC2 distribution.
- Added quantiles to Student-T, Beta and SigmoidBeta, with efficient
implementations for Student-T quantile/cdf.
- Allow structured index points to `GaussianProcess*` classes.
- Improved efficiency of `GaussianProcess*` gradients through custom gradients
on `log_prob`.

* Linear Algebra
- Added functions (with custom gradients) to handle Hermitian Symmetric Positive-definite matrices:
- `tfp.math.hspd_logdet`
- `tfp.math.hpsd_quadratic_form_solve` and `tfp.math.hpsd_quadratic_form_solvevec`
- `tfp.math.hpsd_solve` and `tfp.math.hpsd_solvevec`

* Optimizer
- BUGFIX: Prevent Hager-Zhang linesearch from terminating early.

* PSD Kernels
- Added support for structured inputs in PSD Kernel.

* STS
- Added seasonality support to STS Gibbs Sampler.

* Other
- BUGFIX: Allow jnp.bfloat16 arrays to be correctly recognized as floats.


Huge thanks to all the contributors to this release!

- Brian Patton
- Chen Qian
- Christopher Suter
- Colin Carrol
- Emily Fertig
- Francois Chollet
- Ian Langmore
- Jacob Burnim
- Jonas Eschle
- Kyle Loveless
- Leandro Campos
- Du Phan
- Pavel Sountsov
- Sebastian Nowozin
- Srinivas Vasudevan
- Thomas Colthurst
- Umer Javed
- Urs Koster
- Yash Katariya

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]

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

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

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

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