Update generated api docs for 0.4 release

PiperOrigin-RevId: 214969965

tensorflow_probability/g3doc/api_docs/python/_toc.yaml

@@ -42,6 +42,8 @@ toc:
       path: /probability/api_docs/python/tfp/bijectors/CholeskyToInvCholesky
     - title: ConditionalBijector
       path: /probability/api_docs/python/tfp/bijectors/ConditionalBijector
+    - title: DiscreteCosineTransform
+      path: /probability/api_docs/python/tfp/bijectors/DiscreteCosineTransform
     - title: Exp
       path: /probability/api_docs/python/tfp/bijectors/Exp
     - title: FillTriangular
@@ -160,6 +162,10 @@ toc:
       path: /probability/api_docs/python/tfp/distributions/GaussianProcessRegressionModel
     - title: Geometric
       path: /probability/api_docs/python/tfp/distributions/Geometric
+    - title: Gumbel
+      path: /probability/api_docs/python/tfp/distributions/Gumbel
+    - title: HalfCauchy
+      path: /probability/api_docs/python/tfp/distributions/HalfCauchy
     - title: HalfNormal
       path: /probability/api_docs/python/tfp/distributions/HalfNormal
     - title: Independent
@@ -168,6 +174,8 @@ toc:
       path: /probability/api_docs/python/tfp/distributions/InverseGamma
     - title: InverseGammaWithSoftplusConcentrationRate
       path: /probability/api_docs/python/tfp/distributions/InverseGammaWithSoftplusConcentrationRate
+    - title: InverseGaussian
+      path: /probability/api_docs/python/tfp/distributions/InverseGaussian
     - title: kl_divergence
       path: /probability/api_docs/python/tfp/distributions/kl_divergence
     - title: Kumaraswamy
@@ -218,6 +226,8 @@ toc:
       path: /probability/api_docs/python/tfp/distributions/normal_conjugates_known_scale_predictive
     - title: OneHotCategorical
       path: /probability/api_docs/python/tfp/distributions/OneHotCategorical
+    - title: Pareto
+      path: /probability/api_docs/python/tfp/distributions/Pareto
     - title: percentile
       path: /probability/api_docs/python/tfp/distributions/percentile
     - title: Poisson
@@ -272,6 +282,8 @@ toc:
       path: /probability/api_docs/python/tfp/distributions/VectorLaplaceDiag
     - title: VectorSinhArcsinhDiag
       path: /probability/api_docs/python/tfp/distributions/VectorSinhArcsinhDiag
+    - title: VonMises
+      path: /probability/api_docs/python/tfp/distributions/VonMises
     - title: VonMisesFisher
       path: /probability/api_docs/python/tfp/distributions/VonMisesFisher
     - title: Wishart
@@ -312,12 +324,14 @@ toc:
       path: /probability/api_docs/python/tfp/edward2/Gamma
     - title: Geometric
       path: /probability/api_docs/python/tfp/edward2/Geometric
-    - title: get_interceptor
-      path: /probability/api_docs/python/tfp/edward2/get_interceptor
+    - title: get_next_interceptor
+      path: /probability/api_docs/python/tfp/edward2/get_next_interceptor
     - title: HalfNormal
       path: /probability/api_docs/python/tfp/edward2/HalfNormal
     - title: Independent
       path: /probability/api_docs/python/tfp/edward2/Independent
+    - title: interceptable
+      path: /probability/api_docs/python/tfp/edward2/interceptable
     - title: interception
       path: /probability/api_docs/python/tfp/edward2/interception
     - title: InverseGamma
@@ -364,6 +378,8 @@ toc:
       path: /probability/api_docs/python/tfp/edward2/SinhArcsinh
     - title: StudentT
       path: /probability/api_docs/python/tfp/edward2/StudentT
+    - title: tape
+      path: /probability/api_docs/python/tfp/edward2/tape
     - title: TransformedDistribution
       path: /probability/api_docs/python/tfp/edward2/TransformedDistribution
     - title: Uniform
@@ -398,6 +414,10 @@ toc:
       path: /probability/api_docs/python/tfp/glm/fit
     - title: fit_one_step
       path: /probability/api_docs/python/tfp/glm/fit_one_step
+    - title: fit_sparse
+      path: /probability/api_docs/python/tfp/glm/fit_sparse
+    - title: fit_sparse_one_step
+      path: /probability/api_docs/python/tfp/glm/fit_sparse_one_step
     - title: GammaExp
       path: /probability/api_docs/python/tfp/glm/GammaExp
     - title: GammaSoftplus
@@ -414,6 +434,8 @@ toc:
       path: /probability/api_docs/python/tfp/glm/Poisson
     - title: PoissonSoftplus
       path: /probability/api_docs/python/tfp/glm/PoissonSoftplus
+    - title: soft_threshold
+      path: /probability/api_docs/python/tfp/glm/soft_threshold
   - title: tfp.layers
     section:
     - title: Overview
@@ -446,6 +468,8 @@ toc:
     section:
     - title: Overview
       path: /probability/api_docs/python/tfp/math
+    - title: custom_gradient
+      path: /probability/api_docs/python/tfp/math/custom_gradient
     - title: diag_jacobian
       path: /probability/api_docs/python/tfp/math/diag_jacobian
     - title: matvecmul
@@ -542,6 +566,24 @@ toc:
       path: /probability/api_docs/python/tfp/positive_semidefinite_kernels/MaternThreeHalves
     - title: PositiveSemidefiniteKernel
       path: /probability/api_docs/python/tfp/positive_semidefinite_kernels/PositiveSemidefiniteKernel
+  - title: tfp.sts
+    section:
+    - title: Overview
+      path: /probability/api_docs/python/tfp/sts
+    - title: AdditiveStateSpaceModel
+      path: /probability/api_docs/python/tfp/sts/AdditiveStateSpaceModel
+    - title: LocalLinearTrend
+      path: /probability/api_docs/python/tfp/sts/LocalLinearTrend
+    - title: LocalLinearTrendStateSpaceModel
+      path: /probability/api_docs/python/tfp/sts/LocalLinearTrendStateSpaceModel
+    - title: Seasonal
+      path: /probability/api_docs/python/tfp/sts/Seasonal
+    - title: SeasonalStateSpaceModel
+      path: /probability/api_docs/python/tfp/sts/SeasonalStateSpaceModel
+    - title: StructuralTimeSeries
+      path: /probability/api_docs/python/tfp/sts/StructuralTimeSeries
+    - title: Sum
+      path: /probability/api_docs/python/tfp/sts/Sum
   - title: tfp.trainable_distributions
     section:
     - title: Overview

tensorflow_probability/g3doc/api_docs/python/index.md

@@ -12,6 +12,7 @@
 *  <a href="./tfp/bijectors/CholeskyOuterProduct.md"><code>tfp.bijectors.CholeskyOuterProduct</code></a>
 *  <a href="./tfp/bijectors/CholeskyToInvCholesky.md"><code>tfp.bijectors.CholeskyToInvCholesky</code></a>
 *  <a href="./tfp/bijectors/ConditionalBijector.md"><code>tfp.bijectors.ConditionalBijector</code></a>
+*  <a href="./tfp/bijectors/DiscreteCosineTransform.md"><code>tfp.bijectors.DiscreteCosineTransform</code></a>
 *  <a href="./tfp/bijectors/Exp.md"><code>tfp.bijectors.Exp</code></a>
 *  <a href="./tfp/bijectors/FillTriangular.md"><code>tfp.bijectors.FillTriangular</code></a>
 *  <a href="./tfp/bijectors/Gumbel.md"><code>tfp.bijectors.Gumbel</code></a>
@@ -65,10 +66,13 @@
 *  <a href="./tfp/distributions/GaussianProcess.md"><code>tfp.distributions.GaussianProcess</code></a>
 *  <a href="./tfp/distributions/GaussianProcessRegressionModel.md"><code>tfp.distributions.GaussianProcessRegressionModel</code></a>
 *  <a href="./tfp/distributions/Geometric.md"><code>tfp.distributions.Geometric</code></a>
+*  <a href="./tfp/distributions/Gumbel.md"><code>tfp.distributions.Gumbel</code></a>
+*  <a href="./tfp/distributions/HalfCauchy.md"><code>tfp.distributions.HalfCauchy</code></a>
 *  <a href="./tfp/distributions/HalfNormal.md"><code>tfp.distributions.HalfNormal</code></a>
 *  <a href="./tfp/distributions/Independent.md"><code>tfp.distributions.Independent</code></a>
 *  <a href="./tfp/distributions/InverseGamma.md"><code>tfp.distributions.InverseGamma</code></a>
 *  <a href="./tfp/distributions/InverseGammaWithSoftplusConcentrationRate.md"><code>tfp.distributions.InverseGammaWithSoftplusConcentrationRate</code></a>
+*  <a href="./tfp/distributions/InverseGaussian.md"><code>tfp.distributions.InverseGaussian</code></a>
 *  <a href="./tfp/distributions/Kumaraswamy.md"><code>tfp.distributions.Kumaraswamy</code></a>
 *  <a href="./tfp/distributions/LKJ.md"><code>tfp.distributions.LKJ</code></a>
 *  <a href="./tfp/distributions/Laplace.md"><code>tfp.distributions.Laplace</code></a>
@@ -89,6 +93,7 @@
 *  <a href="./tfp/distributions/Normal.md"><code>tfp.distributions.Normal</code></a>
 *  <a href="./tfp/distributions/NormalWithSoftplusScale.md"><code>tfp.distributions.NormalWithSoftplusScale</code></a>
 *  <a href="./tfp/distributions/OneHotCategorical.md"><code>tfp.distributions.OneHotCategorical</code></a>
+*  <a href="./tfp/distributions/Pareto.md"><code>tfp.distributions.Pareto</code></a>
 *  <a href="./tfp/distributions/Poisson.md"><code>tfp.distributions.Poisson</code></a>
 *  <a href="./tfp/distributions/PoissonLogNormalQuadratureCompound.md"><code>tfp.distributions.PoissonLogNormalQuadratureCompound</code></a>
 *  <a href="./tfp/distributions/QuantizedDistribution.md"><code>tfp.distributions.QuantizedDistribution</code></a>
@@ -108,6 +113,7 @@
 *  <a href="./tfp/distributions/VectorExponentialDiag.md"><code>tfp.distributions.VectorExponentialDiag</code></a>
 *  <a href="./tfp/distributions/VectorLaplaceDiag.md"><code>tfp.distributions.VectorLaplaceDiag</code></a>
 *  <a href="./tfp/distributions/VectorSinhArcsinhDiag.md"><code>tfp.distributions.VectorSinhArcsinhDiag</code></a>
+*  <a href="./tfp/distributions/VonMises.md"><code>tfp.distributions.VonMises</code></a>
 *  <a href="./tfp/distributions/VonMisesFisher.md"><code>tfp.distributions.VonMisesFisher</code></a>
 *  <a href="./tfp/distributions/Wishart.md"><code>tfp.distributions.Wishart</code></a>
 *  <a href="./tfp/distributions/assign_log_moving_mean_exp.md"><code>tfp.distributions.assign_log_moving_mean_exp</code></a>
@@ -176,9 +182,11 @@
 *  <a href="./tfp/edward2/VectorSinhArcsinhDiag.md"><code>tfp.edward2.VectorSinhArcsinhDiag</code></a>
 *  <a href="./tfp/edward2/Wishart.md"><code>tfp.edward2.Wishart</code></a>
 *  <a href="./tfp/edward2/as_random_variable.md"><code>tfp.edward2.as_random_variable</code></a>
-*  <a href="./tfp/edward2/get_interceptor.md"><code>tfp.edward2.get_interceptor</code></a>
+*  <a href="./tfp/edward2/get_next_interceptor.md"><code>tfp.edward2.get_next_interceptor</code></a>
+*  <a href="./tfp/edward2/interceptable.md"><code>tfp.edward2.interceptable</code></a>
 *  <a href="./tfp/edward2/interception.md"><code>tfp.edward2.interception</code></a>
 *  <a href="./tfp/edward2/make_log_joint_fn.md"><code>tfp.edward2.make_log_joint_fn</code></a>
+*  <a href="./tfp/edward2/tape.md"><code>tfp.edward2.tape</code></a>
 *  <a href="./tfp/glm.md"><code>tfp.glm</code></a>
 *  <a href="./tfp/glm/Bernoulli.md"><code>tfp.glm.Bernoulli</code></a>
 *  <a href="./tfp/glm/BernoulliNormalCDF.md"><code>tfp.glm.BernoulliNormalCDF</code></a>
@@ -195,6 +203,9 @@
 *  <a href="./tfp/glm/convergence_criteria_small_relative_norm_weights_change.md"><code>tfp.glm.convergence_criteria_small_relative_norm_weights_change</code></a>
 *  <a href="./tfp/glm/fit.md"><code>tfp.glm.fit</code></a>
 *  <a href="./tfp/glm/fit_one_step.md"><code>tfp.glm.fit_one_step</code></a>
+*  <a href="./tfp/glm/fit_sparse.md"><code>tfp.glm.fit_sparse</code></a>
+*  <a href="./tfp/glm/fit_sparse_one_step.md"><code>tfp.glm.fit_sparse_one_step</code></a>
+*  <a href="./tfp/glm/soft_threshold.md"><code>tfp.glm.soft_threshold</code></a>
 *  <a href="./tfp/layers.md"><code>tfp.layers</code></a>
 *  <a href="./tfp/layers/Convolution1DFlipout.md"><code>tfp.layers.Convolution1DFlipout</code></a>
 *  <a href="./tfp/layers/Convolution1DReparameterization.md"><code>tfp.layers.Convolution1DReparameterization</code></a>
@@ -209,6 +220,7 @@
 *  <a href="./tfp/layers/default_mean_field_normal_fn.md"><code>tfp.layers.default_mean_field_normal_fn</code></a>
 *  <a href="./tfp/layers/default_multivariate_normal_fn.md"><code>tfp.layers.default_multivariate_normal_fn</code></a>
 *  <a href="./tfp/math.md"><code>tfp.math</code></a>
+*  <a href="./tfp/math/custom_gradient.md"><code>tfp.math.custom_gradient</code></a>
 *  <a href="./tfp/math/diag_jacobian.md"><code>tfp.math.diag_jacobian</code></a>
 *  <a href="./tfp/math/matvecmul.md"><code>tfp.math.matvecmul</code></a>
 *  <a href="./tfp/math/pinv.md"><code>tfp.math.pinv</code></a>
@@ -252,6 +264,14 @@
 *  <a href="./tfp/positive_semidefinite_kernels/MaternOneHalf.md"><code>tfp.positive_semidefinite_kernels.MaternOneHalf</code></a>
 *  <a href="./tfp/positive_semidefinite_kernels/MaternThreeHalves.md"><code>tfp.positive_semidefinite_kernels.MaternThreeHalves</code></a>
 *  <a href="./tfp/positive_semidefinite_kernels/PositiveSemidefiniteKernel.md"><code>tfp.positive_semidefinite_kernels.PositiveSemidefiniteKernel</code></a>
+*  <a href="./tfp/sts.md"><code>tfp.sts</code></a>
+*  <a href="./tfp/sts/AdditiveStateSpaceModel.md"><code>tfp.sts.AdditiveStateSpaceModel</code></a>
+*  <a href="./tfp/sts/LocalLinearTrend.md"><code>tfp.sts.LocalLinearTrend</code></a>
+*  <a href="./tfp/sts/LocalLinearTrendStateSpaceModel.md"><code>tfp.sts.LocalLinearTrendStateSpaceModel</code></a>
+*  <a href="./tfp/sts/Seasonal.md"><code>tfp.sts.Seasonal</code></a>
+*  <a href="./tfp/sts/SeasonalStateSpaceModel.md"><code>tfp.sts.SeasonalStateSpaceModel</code></a>
+*  <a href="./tfp/sts/StructuralTimeSeries.md"><code>tfp.sts.StructuralTimeSeries</code></a>
+*  <a href="./tfp/sts/Sum.md"><code>tfp.sts.Sum</code></a>
 *  <a href="./tfp/trainable_distributions.md"><code>tfp.trainable_distributions</code></a>
 *  <a href="./tfp/trainable_distributions/bernoulli.md"><code>tfp.trainable_distributions.bernoulli</code></a>
 *  <a href="./tfp/trainable_distributions/multivariate_normal_tril.md"><code>tfp.trainable_distributions.multivariate_normal_tril</code></a>

tensorflow_probability/g3doc/api_docs/python/tfp.md

@@ -1,5 +1,7 @@
 <div itemscope itemtype="http://developers.google.com/ReferenceObject">
 <meta itemprop="name" content="tfp" />
+<meta itemprop="path" content="Stable" />
+<meta itemprop="property" content="__version__"/>
 </div>
 
 # Module: tfp
@@ -28,9 +30,15 @@ Tools for probabilistic reasoning in TensorFlow.
 
 [`positive_semidefinite_kernels`](./tfp/positive_semidefinite_kernels.md) module: Positive-semidefinite kernels package.
 
+[`sts`](./tfp/sts.md) module: Framework for Bayesian structural time series models.
+
 [`trainable_distributions`](./tfp/trainable_distributions.md) module: Trainable distributions.
 
 [`util`](./tfp/util.md) module: TensorFlow Probability python utilities.
 
 [`vi`](./tfp/vi.md) module: Methods and objectives for variational inference.
 
+## Other Members
+
+<h3 id="__version__"><code>__version__</code></h3>
+

tensorflow_probability/g3doc/api_docs/python/tfp/bijectors.md

@@ -1,5 +1,6 @@
 <div itemscope itemtype="http://developers.google.com/ReferenceObject">
 <meta itemprop="name" content="tfp.bijectors" />
+<meta itemprop="path" content="Stable" />
 <meta itemprop="property" content="__all__"/>
 </div>
 
@@ -29,6 +30,8 @@ Bijector Ops.
 
 [`class ConditionalBijector`](../tfp/bijectors/ConditionalBijector.md): Conditional Bijector is a Bijector that allows intrinsic conditioning.
 
+[`class DiscreteCosineTransform`](../tfp/bijectors/DiscreteCosineTransform.md): Compute `Y = g(X) = DCT(X)`, where DCT type is indicated by the `type` arg.
+
 [`class Exp`](../tfp/bijectors/Exp.md): Compute `Y = g(X) = exp(X)`.
 
 [`class FillTriangular`](../tfp/bijectors/FillTriangular.md): Transforms vectors to triangular.
@@ -87,5 +90,5 @@ Bijector Ops.
 
 ## Other Members
 
-`__all__`
+<h3 id="__all__"><code>__all__</code></h3>
 

tensorflow_probability/g3doc/api_docs/python/tfp/bijectors/AbsoluteValue.md

@@ -1,5 +1,6 @@
 <div itemscope itemtype="http://developers.google.com/ReferenceObject">
 <meta itemprop="name" content="tfp.bijectors.AbsoluteValue" />
+<meta itemprop="path" content="Stable" />
 <meta itemprop="property" content="dtype"/>
 <meta itemprop="property" content="forward_min_event_ndims"/>
 <meta itemprop="property" content="graph_parents"/>
@@ -60,6 +61,26 @@ abs.inverse_log_det_jacobian(0.)
 ==> [0., 0.]
 ```
 
+<h2 id="__init__"><code>__init__</code></h2>
+
+``` python
+__init__(
+    validate_args=False,
+    name='absolute_value'
+)
+```
+
+Instantiates the `AbsoluteValue` bijector.
+
+#### Args:
+
+* <b>`validate_args`</b>: Python `bool` indicating whether arguments should be
+    checked for correctness, in particular whether inputs to `inverse` and
+    `inverse_log_det_jacobian` are non-negative.
+* <b>`name`</b>: Python `str` name given to ops managed by this object.
+
+
+
 ## Properties
 
 <h3 id="dtype"><code>dtype</code></h3>
@@ -101,24 +122,6 @@ Returns True if Tensor arguments will be validated.
 
 ## Methods
 
-<h3 id="__init__"><code>__init__</code></h3>
-
-``` python
-__init__(
-    validate_args=False,
-    name='absolute_value'
-)
-```
-
-Instantiates the `AbsoluteValue` bijector.
-
-#### Args:
-
-* <b>`validate_args`</b>: Python `bool` indicating whether arguments should be
-    checked for correctness, in particular whether inputs to `inverse` and
-    `inverse_log_det_jacobian` are non-negative.
-* <b>`name`</b>: Python `str` name given to ops managed by this object.
-
 <h3 id="forward"><code>forward</code></h3>
 
 ``` python
@@ -209,8 +212,8 @@ Returns both the forward_log_det_jacobian.
 * <b>`event_ndims`</b>: Number of dimensions in the probabilistic events being
     transformed. Must be greater than or equal to
     `self.forward_min_event_ndims`. The result is summed over the final
-    dimensions to produce a scalar Jacobian determinant for each event,
-    i.e. it has shape `x.shape.ndims - event_ndims` dimensions.
+    dimensions to produce a scalar Jacobian determinant for each event, i.e.
+    it has shape `x.shape.ndims - event_ndims` dimensions.
 * <b>`name`</b>: The name to give this op.
 
 
@@ -325,8 +328,8 @@ evaluated at `g^{-1}(y)`.
 * <b>`event_ndims`</b>: Number of dimensions in the probabilistic events being
     transformed. Must be greater than or equal to
     `self.inverse_min_event_ndims`. The result is summed over the final
-    dimensions to produce a scalar Jacobian determinant for each event,
-    i.e. it has shape `y.shape.ndims - event_ndims` dimensions.
+    dimensions to produce a scalar Jacobian determinant for each event, i.e.
+    it has shape `y.shape.ndims - event_ndims` dimensions.
 * <b>`name`</b>: The name to give this op.