tfd_skellam: Skellam distribution.

In rstudio/tfprobability: Interface to 'TensorFlow Probability'

Skellam distribution.

Description

The Skellam distribution is parameterized by two rate parameters, rate1 and rate2. Its samples are defined as:

x ~ Poisson(rate1)
y ~ Poisson(rate2)
z = x - y
z ~ Skellam(rate1, rate2)

where the samples x and y are assumed to be independent.

Usage

tfd_skellam(
  rate1 = NULL,
  rate2 = NULL,
  log_rate1 = NULL,
  log_rate2 = NULL,
  force_probs_to_zero_outside_support = FALSE,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = "Skellam"
)

Arguments

rate1	Floating point tensor, the first rate parameter. rate1 must be positive. Must specify exactly one of rate1 and log_rate1
rate2	Floating point tensor, the second rate parameter. rate must be positive. Must specify exactly one of rate2 and log_rate2.
log_rate1	Floating point tensor, the log of the first rate parameter. Must specify exactly one of rate1 and log_rate1.
log_rate2	Floating point tensor, the log of the second rate parameter. Must specify exactly one of rate2 and log_rate2.
force_probs_to_zero_outside_support	logical. When TRUE, log_prob returns -inf (and prob returns 0) for non-integer inputs. When FALSE, log_prob evaluates the Skellam pmf as a continuous function (note that this function is not itself a normalized probability log-density). Default value: FALSE.
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 mass function (pmf) is,

pmf(k; l1, l2) = (l1 / l2) ** (k / 2) * I_k(2 * sqrt(l1 * l2)) / Z
Z = exp(l1 + l2).

where rate1 = l1, rate2 = l2, Z is the normalizing constant and I_k is the modified bessel function of the first kind.

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_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_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_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()

rstudio/tfprobability documentation built on Sept. 11, 2022, 4:32 a.m.