tfprobability: tfd_poisson – R documentation

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tfd_poisson

Poisson distribution

Description

The Poisson distribution is parameterized by an event rate parameter.

Usage

tfd_poisson(
  rate = NULL,
  log_rate = NULL,
  interpolate_nondiscrete = TRUE,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = "Poisson"
)

Arguments

`rate`	Floating point tensor, the rate parameter. `rate` must be positive. Must specify exactly one of `rate` and `log_rate`.
`log_rate`	Floating point tensor, the log of the rate parameter. Must specify exactly one of `rate` and `log_rate`.
`interpolate_nondiscrete`	Logical. When `FALSE`, `log_prob` returns `-inf` (and `prob` returns `0`) for non-integer inputs. When `TRUE`, `log_prob` evaluates the continuous function `k * log_rate - lgamma(k+1) - rate`, which matches the Poisson pmf at integer arguments `k` (note that this function is not itself a normalized probability log-density). Default value: `TRUE`.
`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; lambda, k >= 0) = (lambda^k / k!) / Z
Z = exp(lambda).

where rate = lambda and Z is the normalizing constant.

Value

a distribution instance.

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_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_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()

tfprobability

Interface to 'TensorFlow Probability'

v0.11.0.0

Apache License (>= 2.0)

Authors

Sigrid Keydana [aut, cre], Daniel Falbel [ctb], Kevin Kuo [ctb] (<https://orcid.org/0000-0001-7803-7901>), RStudio [cph]

Initial release