tfd_bates: Bates distribution.

In tfprobability: Interface to 'TensorFlow Probability'

Bates distribution.

Description

The Bates distribution is the distribution of the average of total_count independent samples from Uniform(low, high). It is parameterized by the interval bounds low and high, and total_count, the number of samples. Although some care has been taken to avoid numerical issues, the pdf, cdf, and log versions thereof may still exhibit numerical instability. They are relatively stable near the tails; however near the mode they are unstable if total_count is greater than about 75 for tf$float64, 25 for tf$float32, and 7 for tf$float16. Beyond these limits a warning will be shown if validate_args=FALSE; otherwise an exception is thrown. For high total_count, consider using a Normal approximation.

Usage

tfd_bates(
  total_count,
  low = 0,
  high = 1,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = "Bates"
)

Arguments

total_count	Non-negative integer-valued Tensor with shape broadcastable to the batch shape [N1,..., Nm], m >= 0. This controls the number of samples of Uniform(low, high) to take the mean of.
low	Floating point Tensor representing the lower bounds of the support. Should be broadcastable to [N1,..., Nm] with m >= 0, the same dtype as total_count, and low < high component-wise, after broadcasting. Defaults to 0.
high	Floating point Tensor representing the upper bounds of the support. Should be broadcastable to [N1,..., Nm] with m >= 0, the same dtype as total_count, and low < high component-wise, after broadcasting. Defaults to 1.
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.

Details

Mathematical Details

The probability density function (pdf) is supported in the interval [low, high]. If [low, high] is the unit interval [0, 1], the pdf is,

pdf(x; n, 0, 1) = ((n / (n-1)!) sum_{k=0}^j (-1)^k (n choose k) (nx - k)^{n-1}

where

• total_count = n,

• j = floor(nx)

• n! is the factorial of n,

• (n choose k) is the binomial coefficient n! / (k!(n - k)!) For arbitrary intervals [low, high], the pdf is,

pdf(x; n, low, high) = pdf((x - low) / (high - low); n, 0, 1) / (high - low)

Value

a distribution instance.

See Also

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

Other distributions: tfd_autoregressive(), tfd_batch_reshape(), 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_same_family(), 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.