tfd_mixture_same_family: Mixture (same-family) distribution

In tfprobability: Interface to 'TensorFlow Probability'

Mixture (same-family) distribution

Description

The MixtureSameFamily distribution implements a (batch of) mixture distribution where all components are from different parameterizations of the same distribution type. It is parameterized by a Categorical "selecting distribution" (over k components) and a components distribution, i.e., a Distribution with a rightmost batch shape (equal to [k]) which indexes each (batch of) component.

Usage

tfd_mixture_same_family(
  mixture_distribution,
  components_distribution,
  reparameterize = FALSE,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = "MixtureSameFamily"
)

Arguments

mixture_distribution	tfp$distributions$Categorical-like instance. Manages the probability of selecting components. The number of categories must match the rightmost batch dimension of the components_distribution. Must have either scalar batch_shape or batch_shape matching components_distribution$batch_shape[:-1].
components_distribution	tfp$distributions$Distribution-like instance. Right-most batch dimension indexes components.
reparameterize	Logical, default FALSE. Whether to reparameterize samples of the distribution using implicit reparameterization gradients (Figurnov et al., 2018). The gradients for the mixture logits are equivalent to the ones described by (Graves, 2016). The gradients for the components parameters are also computed using implicit reparameterization (as opposed to ancestral sampling), meaning that all components are updated every step. Only works when: (1) components_distribution is fully reparameterized; (2) components_distribution is either a scalar distribution or fully factorized (tfd.Independent applied to a scalar distribution); (3) batch shape has a known rank. Experimental, may be slow and produce infs/NaNs.
validate_args	Logical, default FALSE. When TRUE distribution parameters are checked for validity despite possibly degrading runtime performance. When FALSE invalid inputs may silently render incorrect outputs. Default value: FALSE.
allow_nan_stats	Logical, default TRUE. When TRUE, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When FALSE, an exception is raised if one or more of the statistic's batch members are undefined.
name	name prefixed to Ops created by this class.

Value

a distribution instance.

References

• Michael Figurnov, Shakir Mohamed and Andriy Mnih. Implicit reparameterization gradients. In Neural Information Processing Systems, 2018.

• Alex Graves. Stochastic Backpropagation through Mixture Density Distributions. arXiv, 2016.

See Also

For usage examples see e.g. tfd_sample(), tfd_log_prob(), tfd_mean().

Other distributions: tfd_autoregressive(), tfd_batch_reshape(), tfd_bates(), tfd_bernoulli(), tfd_beta_binomial(), tfd_beta(), tfd_binomial(), tfd_categorical(), tfd_cauchy(), tfd_chi2(), tfd_chi(), tfd_cholesky_lkj(), tfd_continuous_bernoulli(), tfd_deterministic(), tfd_dirichlet_multinomial(), tfd_dirichlet(), tfd_empirical(), tfd_exp_gamma(), tfd_exp_inverse_gamma(), tfd_exponential(), tfd_gamma_gamma(), tfd_gamma(), tfd_gaussian_process_regression_model(), tfd_gaussian_process(), tfd_generalized_normal(), tfd_geometric(), tfd_gumbel(), tfd_half_cauchy(), tfd_half_normal(), tfd_hidden_markov_model(), tfd_horseshoe(), tfd_independent(), tfd_inverse_gamma(), tfd_inverse_gaussian(), tfd_johnson_s_u(), tfd_joint_distribution_named_auto_batched(), tfd_joint_distribution_named(), tfd_joint_distribution_sequential_auto_batched(), tfd_joint_distribution_sequential(), tfd_kumaraswamy(), tfd_laplace(), tfd_linear_gaussian_state_space_model(), tfd_lkj(), tfd_log_logistic(), tfd_log_normal(), tfd_logistic(), tfd_mixture(), tfd_multinomial(), tfd_multivariate_normal_diag_plus_low_rank(), tfd_multivariate_normal_diag(), tfd_multivariate_normal_full_covariance(), tfd_multivariate_normal_linear_operator(), tfd_multivariate_normal_tri_l(), tfd_multivariate_student_t_linear_operator(), tfd_negative_binomial(), tfd_normal(), tfd_one_hot_categorical(), tfd_pareto(), tfd_pixel_cnn(), tfd_poisson_log_normal_quadrature_compound(), tfd_poisson(), tfd_power_spherical(), tfd_probit_bernoulli(), tfd_quantized(), tfd_relaxed_bernoulli(), tfd_relaxed_one_hot_categorical(), tfd_sample_distribution(), tfd_sinh_arcsinh(), tfd_skellam(), tfd_spherical_uniform(), tfd_student_t_process(), tfd_student_t(), tfd_transformed_distribution(), tfd_triangular(), tfd_truncated_cauchy(), tfd_truncated_normal(), tfd_uniform(), tfd_variational_gaussian_process(), tfd_vector_diffeomixture(), tfd_vector_exponential_diag(), tfd_vector_exponential_linear_operator(), tfd_vector_laplace_diag(), tfd_vector_laplace_linear_operator(), tfd_vector_sinh_arcsinh_diag(), tfd_von_mises_fisher(), tfd_von_mises(), tfd_weibull(), tfd_wishart_linear_operator(), tfd_wishart_tri_l(), tfd_wishart(), tfd_zipf()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.