Interface to 'TensorFlow Probability'
Log cumulative distribution function.
The log-logistic distribution.
Log-normal distribution
Log probability density/mass function.
Log survival function.
Logistic distribution with location loc and scale parameters
GLM families
Runs multiple Fisher scoring steps
Runs multiple Fisher scoring steps
Runs one Fisher scoring step
Runs one Fisher Scoring step
Blockwise Initializer
Installs TensorFlow Probability
Masked Autoencoder for Distribution Estimation
An autoregressive normalizing flow layer, given a `layer_autoregressiv...
A OneHotCategorical mixture Keras layer from k * (1 + d) params.
1D convolution layer (e.g. temporal convolution) with Flipout
1D convolution layer (e.g. temporal convolution).
2D convolution layer (e.g. spatial convolution over images) with Flipo...
2D convolution layer (e.g. spatial convolution over images)
3D convolution layer (e.g. spatial convolution over volumes) with Flip...
3D convolution layer (e.g. spatial convolution over volumes)
Densely-connected layer class with Flipout estimator.
Densely-connected layer class with local reparameterization estimator.
Densely-connected layer class with reparameterization estimator.
Dense Variational Layer
Keras layer enabling plumbing TFP distributions through Keras models
An Independent-Bernoulli Keras layer from prod(event_shape) params
An independent Logistic Keras layer.
An independent Normal Keras layer.
An independent Poisson Keras layer.
Pass-through layer that adds a KL divergence penalty to the model loss
Regularizer that adds a KL divergence penalty to the model loss
A mixture distribution Keras layer, with independent logistic componen...
A mixture distribution Keras layer, with independent normal components...
A mixture (same-family) Keras layer.
A d-variate Multivariate Normal TriL Keras layer from d+d*(d+1)/ 2 p...
A d-variate OneHotCategorical Keras layer from d params.
Variable Layer
A Variational Gaussian Process Layer.
Adapts the inner kernel's step_size based on log_accept_prob.
Estimate a lower bound on effective sample size for each independent c...
Runs one step of Hamiltonian Monte Carlo.
Runs one step of Metropolis-adjusted Langevin algorithm.
Runs one step of the Metropolis-Hastings algorithm.
Runs one step of the No U-Turn Sampler
Gelman and Rubin (1992)'s potential scale reduction for chain converge...
Runs one step of the RWM algorithm with symmetric proposal.
Runs one step of the Replica Exchange Monte Carlo
Runs annealed importance sampling (AIS) to estimate normalizing consta...
Implements Markov chain Monte Carlo via repeated TransitionKernel st...
Returns a sample from the dim dimensional Halton sequence.
Adapts the inner kernel's step_size based on log_accept_prob.
Runs one step of the slice sampler using a hit and run approach
Applies a bijector to the MCMC's state space
Runs one step of Uncalibrated Hamiltonian Monte Carlo
Runs one step of Uncalibrated Langevin discretized diffusion.
Generate proposal for the Random Walk Metropolis algorithm.
number of params needed to create a CategoricalMixtureOfOneHotCatego...
number of params needed to create an IndependentBernoulli distributi...
number of params needed to create an IndependentLogistic distributio...
number of params needed to create an IndependentNormal distribution
number of params needed to create an IndependentPoisson distribution
number of params needed to create a MixtureLogistic distribution
number of params needed to create a MixtureNormal distribution
number of params needed to create a MixtureSameFamily distribution
number of params needed to create a MultivariateNormalTriL distribut...
number of params needed to create a OneHotCategorical distribution
Objects exported from other packages
A state space model representing a sum of component state space models...
Formal representation of an autoregressive model.
State space model for an autoregressive process.
Build a variational posterior that factors over model parameters.
Build a loss function for variational inference in STS models.
Seasonal state space model with effects constrained to sum to zero.
Decompose an observed time series into contributions from each compone...
Decompose a forecast distribution into contributions from each compone...
Formal representation of a dynamic linear regression model.
State space model for a dynamic linear regression from provided covari...
Draw posterior samples using Hamiltonian Monte Carlo (HMC)
Construct predictive distribution over future observations
Formal representation of a linear regression from provided covariates.
Formal representation of a local level model
State space model for a local level
Formal representation of a local linear trend model
State space model for a local linear trend
Compute one-step-ahead predictive distributions for all timesteps
Initialize from a uniform [-2, 2] distribution in unconstrained spac...
Formal representation of a seasonal effect model.
State space model for a seasonal effect.
Formal representation of a semi-local linear trend model.
State space model for a semi-local linear trend.
Formal representation of a smooth seasonal effect model
State space model for a smooth seasonal effect
Formal representation of a sparse linear regression.
Sum of structural time series components.
ComputesY = g(X) = Abs(X), element-wise
Affine bijector
ComputesY = g(X; shift, scale) = scale @ X + shift
AffineScalar bijector (Deprecated)
Maps unconstrained R^n to R^n in ascending order.
ComputesY = g(X) s.t. X = g^-1(Y) = (Y - mean(Y)) / std(Y)
Bijector which applies a list of bijectors to blocks of a Tensor
Bijector which applies a sequence of bijectors
Computesg(X) = X @ X.T where X is lower-triangular, positive-diago...
Maps the Cholesky factor of M to the Cholesky factor of M^{-1}
Maps unconstrained reals to Cholesky-space correlation matrices.
Computes the cumulative sum of a tensor along a specified axis.
ComputesY = g(X) = DCT(X), where DCT type is indicated by the type a...
ComputesY=g(X)=exp(X)
ComputesY = g(X) = exp(X) - 1
Implements a continuous normalizing flow X->Y defined via an ODE.
Transforms unconstrained vectors to TriL matrices with positive diagon...
Transforms vectors to triangular
Returns the forward Bijector evaluation, i.e., X = g(Y).
Returns the result of the forward evaluation of the log determinant of...
Implements the Glow Bijector from Kingma & Dhariwal (2018).
Compute Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1), the Gompertz CD...
ComputesY = g(X) = exp(-exp(-(X - loc) / scale))
Compute Y = g(X) = exp(-exp(-(X - loc) / scale)), the Gumbel CDF.
ComputesY = g(X) = X
Bijector constructed from custom functions
Returns the inverse Bijector evaluation, i.e., X = g^{-1}(Y).
Returns the result of the inverse evaluation of the log determinant of...
Bijector which inverts another Bijector
Bijector which applies a Stick Breaking procedure.
ComputesY = g(X) = (1 - (1 - X)**(1 / b))**(1 / a), with X in `[0, 1...
ComputesY = g(X) = (1 - (1 - X)**(1 / b))**(1 / a), with X in `[0, 1...
LambertWTail transformation for heavy-tail Lambert W x F random variab...
Masked Autoregressive Density Estimator
Affine MaskedAutoregressiveFlow bijector
Autoregressively masked dense layer
Computes g(L) = inv(L), where L is a lower-triangular matrix
Matrix-vector multiply using LU decomposition
ComputesY = g(X) = NormalCDF(x)
Bijector which maps a tensor x_k that has increasing elements in the l...
Pads a value to the event_shape of a Tensor.
Permutes the rightmost dimension of a Tensor
ComputesY = g(X) = (1 + X * c)**(1 / c), where X >= -1 / c
A piecewise rational quadratic spline, as developed in Conor et al.(20...
Compute Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0.
RealNVP affine coupling layer for vector-valued events
Build a scale-and-shift function using a multi-layer neural network
A Bijector that computes b(x) = 1. / x
Reshapes the event_shape of a Tensor
Compute Y = g(X; scale) = scale * X.
Compute Y = g(X; scale) = scale @ X
Compute Y = g(X; scale) = scale @ X.
Matrix-vector multiply using LU decomposition.
Compute Y = g(X; scale) = scale @ X.
Transforms unconstrained vectors to TriL matrices with positive diagon...
Compute Y = g(X; shift) = X + shift.
Compute Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X))
ComputesY = g(X) = 1 / (1 + exp(-X))
Bijector that computes Y = sinh(X).
ComputesY = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight )
Computes Y = g(X) = exp([X 0]) / sum(exp([X 0]))
Computes Y = g(X) = Log[1 + exp(X)]
Computes Y = g(X) = X / (1 + |X|)
Split a Tensor event along an axis into a list of Tensors.
Computesg(X) = X^2; X is a positive real number.
Computes Y = tanh(X)
Applies a Bijector to the diagonal of a matrix
ComputesY = g(X) = transpose_rightmost_dims(X, rightmost_perm)
ComputesY = g(X) = 1 - exp((-X / scale) ** concentration) where X >=...
Compute Y = g(X) = 1 - exp((-X / scale) ** concentration), X >= 0.
Autoregressive distribution
Batch-Reshaping distribution
Bates distribution.
Bernoulli distribution
Beta distribution
Beta-Binomial compound distribution
Binomial distribution
Blockwise distribution
Categorical distribution over integers
Cauchy distribution with location loc and scale scale
Cumulative distribution function. Given random variable X, the cumulat...
Chi distribution
Chi Square distribution
The CholeskyLKJ distribution on cholesky factors of correlation matric...
Continuous Bernoulli distribution.
Covariance.
Computes the (Shannon) cross entropy.
Scalar Deterministic distribution on the real line
Dirichlet distribution
Dirichlet-Multinomial compound distribution
Double-sided Maxwell distribution.
Empirical distribution
Shannon entropy in nats.
ExpGamma distribution.
ExpInverseGamma distribution.
ExpRelaxedOneHotCategorical distribution with temperature and logits.
Exponential distribution
The finite discrete distribution.
Gamma distribution
Gamma-Gamma distribution
Marginal distribution of a Gaussian process at finitely many points.
Posterior predictive distribution in a conjugate GP regression model.
The Generalized Normal distribution.
The Generalized Pareto distribution.
Geometric distribution
Scalar Gumbel distribution with location loc and scale parameters
Half-Cauchy distribution
Half-Normal distribution with scale scale
Hidden Markov model distribution
Horseshoe distribution
Independent distribution from batch of distributions
InverseGamma distribution
Inverse Gaussian distribution
Johnson's SU-distribution.
Joint distribution parameterized by named distribution-making function...
Joint distribution parameterized by named distribution-making function...
Joint distribution parameterized by distribution-making functions
Joint distribution parameterized by distribution-making functions.
Computes the Kullback--Leibler divergence.
Kumaraswamy distribution
Laplace distribution with location loc and scale parameters
Observation distribution from a linear Gaussian state space model
LKJ distribution on correlation matrices
The Logit-Normal distribution
Mean.
Mixture distribution
Mixture (same-family) distribution
Mode.
Multinomial distribution
Multivariate normal distribution on R^k
Multivariate normal distribution on R^k
Multivariate normal distribution on R^k
The multivariate normal distribution on R^k
The multivariate normal distribution on R^k
Multivariate Student's t-distribution on R^k
NegativeBinomial distribution
Normal distribution with loc and scale parameters
OneHotCategorical distribution
Pareto distribution
Modified PERT distribution for modeling expert predictions.
The Pixel CNN++ distribution
Plackett-Luce distribution over permutations.
Poisson distribution
PoissonLogNormalQuadratureCompound distribution
The Power Spherical distribution over unit vectors on S^{n-1}.
Probability density/mass function.
ProbitBernoulli distribution.
Quantile function. Aka "inverse cdf" or "percent point function".
Distribution representing the quantization Y = ceiling(X)
RelaxedBernoulli distribution with temperature and logits parameters
RelaxedOneHotCategorical distribution with temperature and logits
Generate samples of the specified shape.
Sample distribution via independent draws.
The SinhArcsinh transformation of a distribution on (-inf, inf)
Skellam distribution.
The uniform distribution over unit vectors on S^{n-1}.
Standard deviation.
Student's t-distribution
Marginal distribution of a Student's T process at finitely many points
Survival function.
A Transformed Distribution
Triangular distribution with low, high and peak parameters
The Truncated Cauchy distribution.
Truncated Normal distribution
Uniform distribution with low and high parameters
Variance.
Posterior predictive of a variational Gaussian process
Vector Deterministic Distribution
VectorDiffeomixture distribution
The vectorization of the Exponential distribution on R^k
The vectorization of the Exponential distribution on R^k
The vectorization of the Laplace distribution on R^k
The vectorization of the Laplace distribution on R^k
The (diagonal) SinhArcsinh transformation of a distribution on R^k
The von Mises distribution over angles
The von Mises-Fisher distribution over unit vectors on S^{n-1}
The Weibull distribution with 'concentration' and scale parameters.
The matrix Wishart distribution on positive definite matrices
The matrix Wishart distribution on positive definite matrices
The matrix Wishart distribution parameterized with Cholesky factors.
Zipf distribution
Handle to the tensorflow_probability module
TensorFlow Probability Version
The Amari-alpha Csiszar-function in log-space
The Arithmetic-Geometric Csiszar-function in log-space
The chi-square Csiszar-function in log-space
Use VIMCO to lower the variance of the gradient of csiszar_function(Av...
Calculates the dual Csiszar-function in log-space
Fit a surrogate posterior to a target (unnormalized) log density
The Jeffreys Csiszar-function in log-space
The Jensen-Shannon Csiszar-function in log-space
The forward Kullback-Leibler Csiszar-function in log-space
The reverse Kullback-Leibler Csiszar-function in log-space
The log1p-abs Csiszar-function in log-space
The Modified-GAN Csiszar-function in log-space
Monte-Carlo approximation of an f-Divergence variational loss
The Pearson Csiszar-function in log-space
The Squared-Hellinger Csiszar-function in log-space
Symmetrizes a Csiszar-function in log-space
The T-Power Csiszar-function in log-space
The Total Variation Csiszar-function in log-space
The Triangular Csiszar-function in log-space
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Useful links