tfprobability (version 0.15.2)

tfd_quantized: Distribution representing the quantization Y = ceiling(X)

Description

Definition in Terms of Sampling

Usage

tfd_quantized(
  distribution,
  low = NULL,
  high = NULL,
  validate_args = FALSE,
  name = "QuantizedDistribution"
)

Value

a distribution instance.

Arguments

distribution

The base distribution class to transform. Typically an instance of Distribution.

low

Tensor with same dtype as this distribution and shape able to be added to samples. Should be a whole number. Default NULL. If provided, base distribution's prob should be defined at low.

high

Tensor with same dtype as this distribution and shape able to be added to samples. Should be a whole number. Default NULL. If provided, base distribution's prob should be defined at high - 1. high must be strictly greater than low.

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.

name

name prefixed to Ops created by this class.

Details

1. Draw X
2. Set Y <-- ceiling(X)
3. If Y < low, reset Y <-- low
4. If Y > high, reset Y <-- high
5. Return Y

Definition in Terms of the Probability Mass Function

Given scalar random variable X, we define a discrete random variable Y supported on the integers as follows:

P[Y = j] := P[X <= low],  if j == low,
         := P[X > high - 1],  j == high,
         := 0, if j < low or j > high,
         := P[j - 1 < X <= j],  all other j.

Conceptually, without cutoffs, the quantization process partitions the real line R into half open intervals, and identifies an integer j with the right endpoints:

R = ... (-2, -1](-1, 0](0, 1](1, 2](2, 3](3, 4] ...
j = ...      -1      0     1     2     3     4  ...

P[Y = j] is the mass of X within the jth interval. If low = 0, and high = 2, then the intervals are redrawn and j is re-assigned:

R = (-infty, 0](0, 1](1, infty)
j =          0     1     2

P[Y = j] is still the mass of X within the jth interval.

@section References:

• Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma. PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications. International Conference on Learning Representations_, 2017.

• Aaron van den Oord et al. Parallel WaveNet: Fast High-Fidelity Speech Synthesis. arXiv preprint arXiv:1711.10433, 2017.

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(), tfd_beta_binomial(), tfd_binomial(), tfd_categorical(), tfd_cauchy(), tfd_chi(), tfd_chi2(), tfd_cholesky_lkj(), tfd_continuous_bernoulli(), tfd_deterministic(), tfd_dirichlet(), tfd_dirichlet_multinomial(), tfd_empirical(), tfd_exp_gamma(), tfd_exp_inverse_gamma(), tfd_exponential(), tfd_gamma(), tfd_gamma_gamma(), tfd_gaussian_process(), tfd_gaussian_process_regression_model(), 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(), tfd_joint_distribution_named_auto_batched(), tfd_joint_distribution_sequential(), tfd_joint_distribution_sequential_auto_batched(), tfd_kumaraswamy(), tfd_laplace(), tfd_linear_gaussian_state_space_model(), tfd_lkj(), tfd_log_logistic(), tfd_log_normal(), tfd_logistic(), tfd_mixture(), tfd_mixture_same_family(), tfd_multinomial(), tfd_multivariate_normal_diag(), tfd_multivariate_normal_diag_plus_low_rank(), 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(), tfd_poisson_log_normal_quadrature_compound(), tfd_power_spherical(), tfd_probit_bernoulli(), tfd_relaxed_bernoulli(), tfd_relaxed_one_hot_categorical(), tfd_sample_distribution(), tfd_sinh_arcsinh(), tfd_skellam(), tfd_spherical_uniform(), tfd_student_t(), tfd_student_t_process(), 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(), tfd_von_mises_fisher(), tfd_weibull(), tfd_wishart(), tfd_wishart_linear_operator(), tfd_wishart_tri_l(), tfd_zipf()