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"mcmc_functions" ], "topics": [ "mcmc_effective_sample_size" ] }, { "page": "mcmc_hamiltonian_monte_carlo", "title": "Runs one step of Hamiltonian Monte Carlo.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_hamiltonian_monte_carlo" ] }, { "page": "mcmc_metropolis_adjusted_langevin_algorithm", "title": "Runs one step of Metropolis-adjusted Langevin algorithm.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_metropolis_adjusted_langevin_algorithm" ] }, { "page": "mcmc_metropolis_hastings", "title": "Runs one step of the Metropolis-Hastings algorithm.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_metropolis_hastings" ] }, { "page": "mcmc_no_u_turn_sampler", "title": "Runs one step of the No U-Turn Sampler", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_no_u_turn_sampler" ] }, { "page": "mcmc_potential_scale_reduction", "title": "Gelman and Rubin (1992)'s potential scale reduction for chain convergence.", "concept": [ "mcmc_functions" ], "topics": [ "mcmc_potential_scale_reduction" ] }, { "page": "mcmc_random_walk_metropolis", "title": "Runs one step of the RWM algorithm with symmetric proposal.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_random_walk_metropolis" ] }, { "page": "mcmc_replica_exchange_mc", "title": "Runs one step of the Replica Exchange Monte Carlo", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_replica_exchange_mc" ] }, { "page": "mcmc_sample_annealed_importance_chain", "title": "Runs annealed importance sampling (AIS) to estimate normalizing constants.", "concept": [ "mcmc_functions" ], "topics": [ "mcmc_sample_annealed_importance_chain" ] }, { "page": "mcmc_sample_chain", "title": "Implements Markov chain Monte Carlo via repeated 'TransitionKernel' steps.", "concept": [ "mcmc_functions" ], "topics": [ "mcmc_sample_chain" ] }, { "page": "mcmc_sample_halton_sequence", "title": "Returns a sample from the 'dim' dimensional Halton sequence.", "concept": [ "mcmc_functions" ], "topics": [ "mcmc_sample_halton_sequence" ] }, { "page": "mcmc_simple_step_size_adaptation", "title": "Adapts the inner kernel's 'step_size' based on 'log_accept_prob'.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_simple_step_size_adaptation" ] }, { "page": "mcmc_slice_sampler", "title": "Runs one step of the slice sampler using a hit and run approach", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_slice_sampler" ] }, { "page": "mcmc_transformed_transition_kernel", "title": "Applies a bijector to the MCMC's state space", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_transformed_transition_kernel" ] }, { "page": "mcmc_uncalibrated_hamiltonian_monte_carlo", "title": "Runs one step of Uncalibrated Hamiltonian Monte Carlo", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_uncalibrated_hamiltonian_monte_carlo" ] }, { "page": "mcmc_uncalibrated_langevin", "title": "Runs one step of Uncalibrated Langevin discretized diffusion.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_uncalibrated_langevin" ] }, { "page": "mcmc_uncalibrated_random_walk", "title": "Generate proposal for the Random Walk Metropolis algorithm.", "concept": [ "mcmc_kernels" ], "topics": [ "mcmc_uncalibrated_random_walk" ] }, { "page": "params_size_categorical_mixture_of_one_hot_categorical", "title": "number of 'params' needed to create a CategoricalMixtureOfOneHotCategorical distribution", "topics": [ "params_size_categorical_mixture_of_one_hot_categorical" ] }, { "page": "params_size_independent_bernoulli", "title": "number of 'params' needed to create an IndependentBernoulli distribution", "topics": [ "params_size_independent_bernoulli" ] }, { "page": "params_size_independent_logistic", "title": "number of 'params' needed to create an IndependentLogistic distribution", "topics": [ "params_size_independent_logistic" ] }, { "page": "params_size_independent_normal", "title": "number of 'params' needed to create an IndependentNormal distribution", "topics": [ "params_size_independent_normal" ] }, { "page": "params_size_independent_poisson", "title": "number of 'params' needed to create an IndependentPoisson distribution", "topics": [ "params_size_independent_poisson" ] }, { "page": "params_size_mixture_logistic", "title": "number of 'params' needed to create a MixtureLogistic distribution", "topics": [ "params_size_mixture_logistic" ] }, { "page": "params_size_mixture_normal", "title": "number of 'params' needed to create a MixtureNormal distribution", "topics": [ "params_size_mixture_normal" ] }, { "page": "params_size_mixture_same_family", "title": "number of 'params' needed to create a MixtureSameFamily distribution", "topics": [ "params_size_mixture_same_family" ] }, { "page": "params_size_multivariate_normal_tri_l", "title": "number of 'params' needed to create a MultivariateNormalTriL distribution", "topics": [ "params_size_multivariate_normal_tri_l" ] }, { "page": "params_size_one_hot_categorical", "title": "number of 'params' needed to create a OneHotCategorical distribution", "topics": [ "params_size_one_hot_categorical" ] }, { "page": "sts_additive_state_space_model", "title": "A state space model representing a sum of component state space models.", "concept": [ "sts" ], "topics": [ "sts_additive_state_space_model" ] }, { "page": "sts_autoregressive", "title": "Formal representation of an autoregressive model.", "concept": [ "sts" ], "topics": [ "sts_autoregressive" ] }, { "page": "sts_autoregressive_state_space_model", "title": "State space model for an autoregressive process.", "concept": [ "sts" ], "topics": [ "sts_autoregressive_state_space_model" ] }, { "page": "sts_build_factored_surrogate_posterior", "title": "Build a variational posterior that factors over model parameters.", "concept": [ "sts-functions" ], "topics": [ "sts_build_factored_surrogate_posterior" ] }, { "page": "sts_build_factored_variational_loss", "title": "Build a loss function for variational inference in STS models.", "concept": [ "sts-functions" ], "topics": [ "sts_build_factored_variational_loss" ] }, { "page": "sts_constrained_seasonal_state_space_model", "title": "Seasonal state space model with effects constrained to sum to zero.", "concept": [ "sts" ], "topics": [ "sts_constrained_seasonal_state_space_model" ] }, { "page": "sts_decompose_by_component", "title": "Decompose an observed time series into contributions from each component.", "concept": [ "sts-functions" ], "topics": [ "sts_decompose_by_component" ] }, { "page": "sts_decompose_forecast_by_component", "title": "Decompose a forecast distribution into contributions from each component.", "concept": [ "sts-functions" ], "topics": [ "sts_decompose_forecast_by_component" ] }, { "page": "sts_dynamic_linear_regression", "title": "Formal representation of a dynamic linear regression model.", "concept": [ "sts" ], "topics": [ "sts_dynamic_linear_regression" ] }, { "page": "sts_dynamic_linear_regression_state_space_model", "title": "State space model for a dynamic linear regression from provided covariates.", "concept": [ "sts" ], "topics": [ "sts_dynamic_linear_regression_state_space_model" ] }, { "page": "sts_fit_with_hmc", "title": "Draw posterior samples using Hamiltonian Monte Carlo (HMC)", "concept": [ "sts-functions" ], "topics": [ "sts_fit_with_hmc" ] }, { "page": "sts_forecast", "title": "Construct predictive distribution over future observations", "concept": [ "sts-functions" ], "topics": [ "sts_forecast" ] }, { "page": "sts_linear_regression", "title": "Formal representation of a linear regression from provided covariates.", "concept": [ "sts" ], "topics": [ "sts_linear_regression" ] }, { "page": "sts_local_level", "title": "Formal representation of a local level model", "concept": [ "sts" ], "topics": [ "sts_local_level" ] }, { "page": "sts_local_level_state_space_model", "title": "State space model for a local level", "concept": [ "sts" ], "topics": [ "sts_local_level_state_space_model" ] }, { "page": "sts_local_linear_trend", "title": "Formal representation of a local linear trend model", "concept": [ "sts" ], "topics": [ "sts_local_linear_trend" ] }, { "page": "sts_local_linear_trend_state_space_model", "title": "State space model for a local linear trend", "concept": [ "sts" ], "topics": [ "sts_local_linear_trend_state_space_model" ] }, { "page": "sts_one_step_predictive", "title": "Compute one-step-ahead predictive distributions for all timesteps", "concept": [ "sts-functions" ], "topics": [ "sts_one_step_predictive" ] }, { "page": "sts_sample_uniform_initial_state", "title": "Initialize from a uniform [-2, 2] distribution in unconstrained space.", "concept": [ "sts-functions" ], "topics": [ "sts_sample_uniform_initial_state" ] }, { "page": "sts_seasonal", "title": "Formal representation of a seasonal effect model.", "concept": [ "sts" ], "topics": [ "sts_seasonal" ] }, { "page": "sts_seasonal_state_space_model", "title": "State space model for a seasonal effect.", "concept": [ "sts" ], "topics": [ "sts_seasonal_state_space_model" ] }, { "page": "sts_semi_local_linear_trend", "title": "Formal representation of a semi-local linear trend model.", "concept": [ "sts" ], "topics": [ "sts_semi_local_linear_trend" ] }, { "page": "sts_semi_local_linear_trend_state_space_model", "title": "State space model for a semi-local linear trend.", "concept": [ "sts" ], "topics": [ "sts_semi_local_linear_trend_state_space_model" ] }, { "page": "sts_smooth_seasonal", "title": "Formal representation of a smooth seasonal effect model", "concept": [ "sts" ], "topics": [ "sts_smooth_seasonal" ] }, { "page": "sts_smooth_seasonal_state_space_model", "title": "State space model for a smooth seasonal effect", "concept": [ "sts" ], "topics": [ "sts_smooth_seasonal_state_space_model" ] }, { "page": "sts_sparse_linear_regression", "title": "Formal representation of a sparse linear regression.", "concept": [ "sts" ], "topics": [ "sts_sparse_linear_regression" ] }, { "page": "sts_sum", "title": "Sum of structural time series components.", "concept": [ "sts" ], "topics": [ "sts_sum" ] }, { "page": "tfb_absolute_value", "title": "Computes'Y = g(X) = Abs(X)', element-wise", "concept": [ "bijectors" ], "topics": [ "tfb_absolute_value" ] }, { "page": "tfb_affine", "title": "Affine bijector", "concept": [ "bijectors" ], "topics": [ "tfb_affine" ] }, { "page": "tfb_affine_linear_operator", "title": "ComputesY = g(X; shift, scale) = scale @ X + shift", "concept": [ "bijectors" ], "topics": [ "tfb_affine_linear_operator" ] }, { "page": "tfb_ascending", "title": "Maps unconstrained R^n to R^n in ascending order.", "concept": [ "bijectors" ], "topics": [ "tfb_ascending" ] }, { "page": "tfb_batch_normalization", "title": "Computes'Y = g(X)' s.t. 'X = g^-1(Y) = (Y - mean(Y)) / std(Y)'", "concept": [ "bijectors" ], "topics": [ "tfb_batch_normalization" ] }, { "page": "tfb_blockwise", "title": "Bijector which applies a list of bijectors to blocks of a Tensor", "concept": [ "bijectors" ], "topics": [ "tfb_blockwise" ] }, { "page": "tfb_chain", "title": "Bijector which applies a sequence of bijectors", "concept": [ "bijectors" ], "topics": [ "tfb_chain" ] }, { "page": "tfb_cholesky_outer_product", "title": "Computes'g(X) = X @ X.T' where 'X' is lower-triangular, positive-diagonal matrix", "concept": [ "bijectors" ], "topics": [ "tfb_cholesky_outer_product" ] }, { "page": "tfb_cholesky_to_inv_cholesky", "title": "Maps the Cholesky factor of M to the Cholesky factor of 'M^{-1}'", "concept": [ "bijectors" ], "topics": [ "tfb_cholesky_to_inv_cholesky" ] }, { "page": "tfb_correlation_cholesky", "title": "Maps unconstrained reals to Cholesky-space correlation matrices.", "concept": [ "bijectors" ], "topics": [ "tfb_correlation_cholesky" ] }, { "page": "tfb_cumsum", "title": "Computes the cumulative sum of a tensor along a specified axis.", "concept": [ "bijectors" ], "topics": [ "tfb_cumsum" ] }, { "page": "tfb_discrete_cosine_transform", "title": "Computes'Y = g(X) = DCT(X)', where DCT type is indicated by the type arg", "concept": [ "bijectors" ], "topics": [ "tfb_discrete_cosine_transform" ] }, { "page": "tfb_exp", "title": "Computes'Y=g(X)=exp(X)'", "concept": [ "bijectors" ], "topics": [ "tfb_exp" ] }, { "page": "tfb_expm1", "title": "Computes'Y = g(X) = exp(X) - 1'", "concept": [ "bijectors" ], "topics": [ "tfb_expm1" ] }, { "page": "tfb_ffjord", "title": "Implements a continuous normalizing flow X->Y defined via an ODE.", "concept": [ "bijectors" ], "topics": [ "tfb_ffjord" ] }, { "page": "tfb_fill_scale_tri_l", "title": "Transforms unconstrained vectors to TriL matrices with positive diagonal", "concept": [ "bijectors" ], "topics": [ "tfb_fill_scale_tri_l" ] }, { "page": "tfb_fill_triangular", "title": "Transforms vectors to triangular", "concept": [ "bijectors" ], "topics": [ "tfb_fill_triangular" ] }, { "page": "tfb_forward", "title": "Returns the forward Bijector evaluation, i.e., 'X = g(Y)'.", "concept": [ "bijector_methods" ], "topics": [ "tfb_forward" ] }, { "page": "tfb_forward_log_det_jacobian", "title": "Returns the result of the forward evaluation of the log determinant of the Jacobian", "concept": [ "bijector_methods" ], "topics": [ "tfb_forward_log_det_jacobian" ] }, { "page": "tfb_glow", "title": "Implements the Glow Bijector from Kingma & Dhariwal (2018).", "concept": [ "bijectors" ], "topics": [ "tfb_glow" ] }, { "page": "tfb_gompertz_cdf", "title": "Compute Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1), the Gompertz CDF.", "concept": [ "bijectors" ], "topics": [ "tfb_gompertz_cdf" ] }, { "page": "tfb_gumbel", "title": "Computes'Y = g(X) = exp(-exp(-(X - loc) / scale))'", "concept": [ "bijectors" ], "topics": [ "tfb_gumbel" ] }, { "page": "tfb_gumbel_cdf", "title": "Compute 'Y = g(X) = exp(-exp(-(X - loc) / scale))', the Gumbel CDF.", "concept": [ "bijectors" ], "topics": [ "tfb_gumbel_cdf" ] }, { "page": "tfb_identity", "title": "Computes'Y = g(X) = X'", "concept": [ "bijectors" ], "topics": [ "tfb_identity" ] }, { "page": "tfb_inline", "title": "Bijector constructed from custom functions", "concept": [ "bijectors" ], "topics": [ "tfb_inline" ] }, { "page": "tfb_inverse", "title": "Returns the inverse Bijector evaluation, i.e., 'X = g^{-1}(Y)'.", "concept": [ "bijector_methods" ], "topics": [ "tfb_inverse" ] }, { "page": "tfb_inverse_log_det_jacobian", "title": "Returns the result of the inverse evaluation of the log determinant of the Jacobian", "concept": [ "bijector_methods" ], "topics": [ "tfb_inverse_log_det_jacobian" ] }, { "page": "tfb_invert", "title": "Bijector which inverts another Bijector", "concept": [ "bijectors" ], "topics": [ "tfb_invert" ] }, { "page": "tfb_iterated_sigmoid_centered", "title": "Bijector which applies a Stick Breaking procedure.", "concept": [ "bijectors" ], "topics": [ "tfb_iterated_sigmoid_centered" ] }, { "page": "tfb_kumaraswamy", "title": "Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X in [0, 1]", "concept": [ "bijectors" ], "topics": [ "tfb_kumaraswamy" ] }, { "page": "tfb_kumaraswamy_cdf", "title": "Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X in [0, 1]", "concept": [ "bijectors" ], "topics": [ "tfb_kumaraswamy_cdf" ] }, { "page": "tfb_lambert_w_tail", "title": "LambertWTail transformation for heavy-tail Lambert W x F random variables.", "concept": [ "bijectors" ], "topics": [ "tfb_lambert_w_tail" ] }, { "page": "tfb_masked_autoregressive_default_template", "title": "Masked Autoregressive Density Estimator", "concept": [ "bijectors" ], "topics": [ "tfb_masked_autoregressive_default_template" ] }, { "page": "tfb_masked_autoregressive_flow", "title": "Affine MaskedAutoregressiveFlow bijector", "concept": [ "bijectors" ], "topics": [ "tfb_masked_autoregressive_flow" ] }, { "page": "tfb_masked_dense", "title": "Autoregressively masked dense layer", "concept": [ "bijectors" ], "topics": [ "tfb_masked_dense" ] }, { "page": "tfb_matrix_inverse_tri_l", "title": "Computes 'g(L) = inv(L)', where L is a lower-triangular matrix", "concept": [ "bijectors" ], "topics": [ "tfb_matrix_inverse_tri_l" ] }, { "page": "tfb_matvec_lu", "title": "Matrix-vector multiply using LU decomposition", "concept": [ "bijectors" ], "topics": [ "tfb_matvec_lu" ] }, { "page": "tfb_normal_cdf", "title": "Computes'Y = g(X) = NormalCDF(x)'", "concept": [ "bijectors" ], "topics": [ "tfb_normal_cdf" ] }, { "page": "tfb_ordered", "title": "Bijector which maps a tensor x_k that has increasing elements in the last dimension to an unconstrained tensor y_k", "concept": [ "bijectors" ], "topics": [ "tfb_ordered" ] }, { "page": "tfb_pad", "title": "Pads a value to the 'event_shape' of a 'Tensor'.", "concept": [ "bijectors" ], "topics": [ "tfb_pad" ] }, { "page": "tfb_permute", "title": "Permutes the rightmost dimension of a Tensor", "concept": [ "bijectors" ], "topics": [ "tfb_permute" ] }, { "page": "tfb_power_transform", "title": "Computes'Y = g(X) = (1 + X * c)**(1 / c)', where 'X >= -1 / c'", "concept": [ "bijectors" ], "topics": [ "tfb_power_transform" ] }, { "page": "tfb_rational_quadratic_spline", "title": "A piecewise rational quadratic spline, as developed in Conor et al.(2019).", "concept": [ "bijectors" ], "topics": [ "tfb_rational_quadratic_spline" ] }, { "page": "tfb_rayleigh_cdf", "title": "Compute Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0.", "concept": [ "bijectors" ], "topics": [ "tfb_rayleigh_cdf" ] }, { "page": "tfb_real_nvp", "title": "RealNVP affine coupling layer for vector-valued events", "concept": [ "bijectors" ], "topics": [ "tfb_real_nvp" ] }, { "page": "tfb_real_nvp_default_template", "title": "Build a scale-and-shift function using a multi-layer neural network", "concept": [ "bijectors" ], "topics": [ "tfb_real_nvp_default_template" ] }, { "page": "tfb_reciprocal", "title": "A Bijector that computes 'b(x) = 1. / x'", "concept": [ "bijectors" ], "topics": [ "tfb_reciprocal" ] }, { "page": "tfb_reshape", "title": "Reshapes the event_shape of a Tensor", "concept": [ "bijectors" ], "topics": [ "tfb_reshape" ] }, { "page": "tfb_scale", "title": "Compute Y = g(X; scale) = scale * X.", "concept": [ "bijectors" ], "topics": [ "tfb_scale" ] }, { "page": "tfb_scale_matvec_diag", "title": "Compute Y = g(X; scale) = scale @ X", "concept": [ "bijectors" ], "topics": [ "tfb_scale_matvec_diag" ] }, { "page": "tfb_scale_matvec_linear_operator", "title": "Compute Y = g(X; scale) = scale @ X.", "concept": [ "bijectors" ], "topics": [ "tfb_scale_matvec_linear_operator" ] }, { "page": "tfb_scale_matvec_lu", "title": "Matrix-vector multiply using LU decomposition.", "concept": [ "bijectors" ], "topics": [ "tfb_scale_matvec_lu" ] }, { "page": "tfb_scale_matvec_tri_l", "title": "Compute Y = g(X; scale) = scale @ X.", "concept": [ "bijectors" ], "topics": [ "tfb_scale_matvec_tri_l" ] }, { "page": "tfb_scale_tri_l", "title": "Transforms unconstrained vectors to TriL matrices with positive diagonal", "concept": [ "bijectors" ], "topics": [ "tfb_scale_tri_l" ] }, { "page": "tfb_shift", "title": "Compute Y = g(X; shift) = X + shift.", "concept": [ "bijectors" ], "topics": [ "tfb_shift" ] }, { "page": "tfb_shifted_gompertz_cdf", "title": "Compute 'Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X))'", "concept": [ "bijectors" ], "topics": [ "tfb_shifted_gompertz_cdf" ] }, { "page": "tfb_sigmoid", "title": "Computes'Y = g(X) = 1 / (1 + exp(-X))'", "concept": [ "bijectors" ], "topics": [ "tfb_sigmoid" ] }, { "page": "tfb_sinh", "title": "Bijector that computes 'Y = sinh(X)'.", "concept": [ "bijectors" ], "topics": [ "tfb_sinh" ] }, { "page": "tfb_sinh_arcsinh", "title": "Computes'Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight )'", "concept": [ "bijectors" ], "topics": [ "tfb_sinh_arcsinh" ] }, { "page": "tfb_softmax_centered", "title": "Computes Y = g(X) = exp([X 0]) / sum(exp([X 0]))", "concept": [ "bijectors" ], "topics": [ "tfb_softmax_centered" ] }, { "page": "tfb_softplus", "title": "Computes 'Y = g(X) = Log[1 + exp(X)]'", "concept": [ "bijectors" ], "topics": [ "tfb_softplus" ] }, { "page": "tfb_softsign", "title": "Computes Y = g(X) = X / (1 + |X|)", "concept": [ "bijectors" ], "topics": [ "tfb_softsign" ] }, { "page": "tfb_split", "title": "Split a 'Tensor' event along an axis into a list of 'Tensor's.", "concept": [ "bijectors" ], "topics": [ "tfb_split" ] }, { "page": "tfb_square", "title": "Computes'g(X) = X^2'; X is a positive real number.", "concept": [ "bijectors" ], "topics": [ "tfb_square" ] }, { "page": "tfb_tanh", "title": "Computes 'Y = tanh(X)'", "concept": [ "bijectors" ], "topics": [ "tfb_tanh" ] }, { "page": "tfb_transform_diagonal", "title": "Applies a Bijector to the diagonal of a matrix", "concept": [ "bijectors" ], "topics": [ "tfb_transform_diagonal" ] }, { "page": "tfb_transpose", "title": "Computes'Y = g(X) = transpose_rightmost_dims(X, rightmost_perm)'", "concept": [ "bijectors" ], "topics": [ "tfb_transpose" ] }, { "page": "tfb_weibull", "title": "Computes'Y = g(X) = 1 - exp((-X / scale) ** concentration)' where X >= 0", "concept": [ "bijectors" ], "topics": [ "tfb_weibull" ] }, { "page": "tfb_weibull_cdf", "title": "Compute Y = g(X) = 1 - exp((-X / scale) ** concentration), X >= 0.", "concept": [ "bijectors" ], "topics": [ "tfb_weibull_cdf" ] }, { "page": "tfd_autoregressive", "title": "Autoregressive distribution", "concept": [ "distributions" ], "topics": [ "tfd_autoregressive" ] }, { "page": "tfd_batch_reshape", "title": "Batch-Reshaping distribution", "concept": [ "distributions" ], "topics": [ "tfd_batch_reshape" ] }, { "page": "tfd_bates", "title": "Bates distribution.", "concept": [ "distributions" ], "topics": [ "tfd_bates" ] }, { "page": "tfd_bernoulli", "title": "Bernoulli distribution", "concept": [ "distributions" ], "topics": [ "tfd_bernoulli" ] }, { "page": "tfd_beta", "title": "Beta distribution", "concept": [ "distributions" ], "topics": [ "tfd_beta" ] }, { "page": "tfd_beta_binomial", "title": "Beta-Binomial compound distribution", "concept": [ "distributions" ], "topics": [ "tfd_beta_binomial" ] }, { "page": "tfd_binomial", "title": "Binomial distribution", "concept": [ "distributions" ], "topics": [ "tfd_binomial" ] }, { "page": "tfd_blockwise", "title": "Blockwise distribution", "topics": [ "tfd_blockwise" ] }, { "page": "tfd_categorical", "title": "Categorical distribution over integers", "concept": [ "distributions" ], "topics": [ "tfd_categorical" ] }, { "page": "tfd_cauchy", "title": "Cauchy distribution with location 'loc' and scale 'scale'", "concept": [ "distributions" ], "topics": [ "tfd_cauchy" ] }, { "page": "tfd_cdf", "title": "Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: 'cdf(x) := P[X <= x]'", "concept": [ "distribution_methods" ], "topics": [ "tfd_cdf" ] }, { "page": "tfd_chi", "title": "Chi distribution", "concept": [ "distributions" ], "topics": [ "tfd_chi" ] }, { "page": "tfd_chi2", "title": "Chi Square distribution", "concept": [ "distributions" ], "topics": [ "tfd_chi2" ] }, { "page": "tfd_cholesky_lkj", "title": "The CholeskyLKJ distribution on cholesky factors of correlation matrices", "concept": [ "distributions" ], "topics": [ "tfd_cholesky_lkj" ] }, { "page": "tfd_continuous_bernoulli", "title": "Continuous Bernoulli distribution.", "concept": [ "distributions" ], "topics": [ "tfd_continuous_bernoulli" ] }, { "page": "tfd_covariance", "title": "Covariance.", "concept": [ "distribution_methods" ], "topics": [ "tfd_covariance" ] }, { "page": "tfd_cross_entropy", "title": "Computes the (Shannon) cross entropy.", "concept": [ "distribution_methods" ], "topics": [ "tfd_cross_entropy" ] }, { "page": "tfd_deterministic", "title": "Scalar 'Deterministic' distribution on the real line", "concept": [ "distributions" ], "topics": [ "tfd_deterministic" ] }, { "page": "tfd_dirichlet", "title": "Dirichlet distribution", "concept": [ "distributions" ], "topics": [ "tfd_dirichlet" ] }, { "page": "tfd_dirichlet_multinomial", "title": "Dirichlet-Multinomial compound distribution", "concept": [ "distributions" ], "topics": [ "tfd_dirichlet_multinomial" ] }, { "page": "tfd_doublesided_maxwell", "title": "Double-sided Maxwell distribution.", "topics": [ "tfd_doublesided_maxwell" ] }, { "page": "tfd_empirical", "title": "Empirical distribution", "concept": [ "distributions" ], "topics": [ "tfd_empirical" ] }, { "page": "tfd_entropy", "title": "Shannon entropy in nats.", "concept": [ "distribution_methods" ], "topics": [ "tfd_entropy" ] }, { "page": "tfd_exp_gamma", "title": "ExpGamma distribution.", "concept": [ "distributions" ], "topics": [ "tfd_exp_gamma" ] }, { "page": "tfd_exp_inverse_gamma", "title": "ExpInverseGamma distribution.", "concept": [ "distributions" ], "topics": [ "tfd_exp_inverse_gamma" ] }, { "page": "tfd_exp_relaxed_one_hot_categorical", "title": "ExpRelaxedOneHotCategorical distribution with temperature and logits.", "topics": [ "tfd_exp_relaxed_one_hot_categorical" ] }, { "page": "tfd_exponential", "title": "Exponential distribution", "concept": [ "distributions" ], "topics": [ "tfd_exponential" ] }, { "page": "tfd_finite_discrete", "title": "The finite discrete distribution.", "topics": [ "tfd_finite_discrete" ] }, { "page": "tfd_gamma", "title": "Gamma distribution", "concept": [ "distributions" ], "topics": [ "tfd_gamma" ] }, { "page": "tfd_gamma_gamma", "title": "Gamma-Gamma distribution", "concept": [ "distributions" ], "topics": [ "tfd_gamma_gamma" ] }, { "page": "tfd_gaussian_process", "title": "Marginal distribution of a Gaussian process at finitely many points.", "concept": [ "distributions" ], "topics": [ "tfd_gaussian_process" ] }, { "page": "tfd_gaussian_process_regression_model", "title": "Posterior predictive distribution in a conjugate GP regression model.", "concept": [ "distributions" ], "topics": [ "tfd_gaussian_process_regression_model" ] }, { "page": "tfd_generalized_normal", "title": "The Generalized Normal distribution.", "concept": [ "distributions" ], "topics": [ "tfd_generalized_normal" ] }, { "page": "tfd_generalized_pareto", "title": "The Generalized Pareto distribution.", "topics": [ "tfd_generalized_pareto" ] }, { "page": "tfd_geometric", "title": "Geometric distribution", "concept": [ "distributions" ], "topics": [ "tfd_geometric" ] }, { "page": "tfd_gumbel", "title": "Scalar Gumbel distribution with location 'loc' and 'scale' parameters", "concept": [ "distributions" ], "topics": [ "tfd_gumbel" ] }, { "page": "tfd_half_cauchy", "title": "Half-Cauchy distribution", "concept": [ "distributions" ], "topics": [ "tfd_half_cauchy" ] }, { "page": "tfd_half_normal", "title": "Half-Normal distribution with scale 'scale'", "concept": [ "distributions" ], "topics": [ "tfd_half_normal" ] }, { "page": "tfd_hidden_markov_model", "title": "Hidden Markov model distribution", "concept": [ "distributions" ], "topics": [ "tfd_hidden_markov_model" ] }, { "page": "tfd_horseshoe", "title": "Horseshoe distribution", "concept": [ "distributions" ], "topics": [ "tfd_horseshoe" ] }, { "page": "tfd_independent", "title": "Independent distribution from batch of distributions", "concept": [ "distributions" ], "topics": [ "tfd_independent" ] }, { "page": "tfd_inverse_gamma", "title": "InverseGamma distribution", "concept": [ "distributions" ], "topics": [ "tfd_inverse_gamma" ] }, { "page": "tfd_inverse_gaussian", "title": "Inverse Gaussian distribution", "concept": [ "distributions" ], "topics": [ "tfd_inverse_gaussian" ] }, { "page": "tfd_johnson_s_u", "title": "Johnson's SU-distribution.", "concept": [ "distributions" ], "topics": [ "tfd_johnson_s_u" ] }, { "page": "tfd_joint_distribution_named", "title": "Joint distribution parameterized by named distribution-making functions.", "concept": [ "distributions" ], "topics": [ "tfd_joint_distribution_named" ] }, { "page": "tfd_joint_distribution_named_auto_batched", "title": "Joint distribution parameterized by named distribution-making functions.", "concept": [ "distributions" ], "topics": [ "tfd_joint_distribution_named_auto_batched" ] }, { "page": "tfd_joint_distribution_sequential", "title": "Joint distribution parameterized by distribution-making functions", "concept": [ "distributions" ], "topics": [ "tfd_joint_distribution_sequential" ] }, { "page": "tfd_joint_distribution_sequential_auto_batched", "title": "Joint distribution parameterized by distribution-making functions.", "concept": [ "distributions" ], "topics": [ "tfd_joint_distribution_sequential_auto_batched" ] }, { "page": "tfd_kl_divergence", "title": "Computes the Kullback-Leibler divergence.", "concept": [ "distribution_methods" ], "topics": [ "tfd_kl_divergence" ] }, { "page": "tfd_kumaraswamy", "title": "Kumaraswamy distribution", "concept": [ "distributions" ], "topics": [ "tfd_kumaraswamy" ] }, { "page": "tfd_laplace", "title": "Laplace distribution with location 'loc' and 'scale' parameters", "concept": [ "distributions" ], "topics": [ "tfd_laplace" ] }, { "page": "tfd_linear_gaussian_state_space_model", "title": "Observation distribution from a linear Gaussian state space model", "concept": [ "distributions" ], "topics": [ "tfd_linear_gaussian_state_space_model" ] }, { "page": "tfd_lkj", "title": "LKJ distribution on correlation matrices", "concept": [ "distributions" ], "topics": [ "tfd_lkj" ] }, { "page": "tfd_log_cdf", "title": "Log cumulative distribution function.", "concept": [ "distribution_methods" ], "topics": [ "tfd_log_cdf" ] }, { "page": "tfd_log_logistic", "title": "The log-logistic distribution.", "concept": [ "distributions" ], "topics": [ "tfd_log_logistic" ] }, { "page": "tfd_log_normal", "title": "Log-normal distribution", "concept": [ "distributions" ], "topics": [ "tfd_log_normal" ] }, { "page": "tfd_log_prob", "title": "Log probability density/mass function.", "concept": [ "distribution_methods" ], "topics": [ "tfd_log_prob" ] }, { "page": "tfd_log_survival_function", "title": "Log survival function.", "concept": [ "distribution_methods" ], "topics": [ "tfd_log_survival_function" ] }, { "page": "tfd_logistic", "title": "Logistic distribution with location 'loc' and 'scale' parameters", "concept": [ "distributions" ], "topics": [ "tfd_logistic" ] }, { "page": "tfd_logit_normal", "title": "The Logit-Normal distribution", "topics": [ "tfd_logit_normal" ] }, { "page": "tfd_mean", "title": "Mean.", "concept": [ "distribution_methods" ], "topics": [ "tfd_mean" ] }, { "page": "tfd_mixture", "title": "Mixture distribution", "concept": [ "distributions" ], "topics": [ "tfd_mixture" ] }, { "page": "tfd_mixture_same_family", "title": "Mixture (same-family) distribution", "concept": [ "distributions" ], "topics": [ "tfd_mixture_same_family" ] }, { "page": "tfd_mode", "title": "Mode.", "concept": [ "distribution_methods" ], "topics": [ "tfd_mode" ] }, { "page": "tfd_multinomial", "title": "Multinomial distribution", "concept": [ "distributions" ], "topics": [ "tfd_multinomial" ] }, { "page": "tfd_multivariate_normal_diag", "title": "Multivariate normal distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_multivariate_normal_diag" ] }, { "page": "tfd_multivariate_normal_diag_plus_low_rank", "title": "Multivariate normal distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_multivariate_normal_diag_plus_low_rank" ] }, { "page": "tfd_multivariate_normal_full_covariance", "title": "Multivariate normal distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_multivariate_normal_full_covariance" ] }, { "page": "tfd_multivariate_normal_linear_operator", "title": "The multivariate normal distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_multivariate_normal_linear_operator" ] }, { "page": "tfd_multivariate_normal_tri_l", "title": "The multivariate normal distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_multivariate_normal_tri_l" ] }, { "page": "tfd_multivariate_student_t_linear_operator", "title": "Multivariate Student's t-distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_multivariate_student_t_linear_operator" ] }, { "page": "tfd_negative_binomial", "title": "NegativeBinomial distribution", "concept": [ "distributions" ], "topics": [ "tfd_negative_binomial" ] }, { "page": "tfd_normal", "title": "Normal distribution with loc and scale parameters", "concept": [ "distributions" ], "topics": [ "tfd_normal" ] }, { "page": "tfd_one_hot_categorical", "title": "OneHotCategorical distribution", "concept": [ "distributions" ], "topics": [ "tfd_one_hot_categorical" ] }, { "page": "tfd_pareto", "title": "Pareto distribution", "concept": [ "distributions" ], "topics": [ "tfd_pareto" ] }, { "page": "tfd_pert", "title": "Modified PERT distribution for modeling expert predictions.", "topics": [ "tfd_pert" ] }, { "page": "tfd_pixel_cnn", "title": "The Pixel CNN++ distribution", "concept": [ "distributions" ], "topics": [ "tfd_pixel_cnn" ] }, { "page": "tfd_plackett_luce", "title": "Plackett-Luce distribution over permutations.", "topics": [ "tfd_plackett_luce" ] }, { "page": "tfd_poisson", "title": "Poisson distribution", "concept": [ "distributions" ], "topics": [ "tfd_poisson" ] }, { "page": "tfd_poisson_log_normal_quadrature_compound", "title": "'PoissonLogNormalQuadratureCompound' distribution", "concept": [ "distributions" ], "topics": [ "tfd_poisson_log_normal_quadrature_compound" ] }, { "page": "tfd_power_spherical", "title": "The Power Spherical distribution over unit vectors on 'S^{n-1}'.", "concept": [ "distributions" ], "topics": [ "tfd_power_spherical" ] }, { "page": "tfd_prob", "title": "Probability density/mass function.", "concept": [ "distribution_methods" ], "topics": [ "tfd_prob" ] }, { "page": "tfd_probit_bernoulli", "title": "ProbitBernoulli distribution.", "concept": [ "distributions" ], "topics": [ "tfd_probit_bernoulli" ] }, { "page": "tfd_quantile", "title": "Quantile function. Aka \"inverse cdf\" or \"percent point function\".", "concept": [ "distribution_methods" ], "topics": [ "tfd_quantile" ] }, { "page": "tfd_quantized", "title": "Distribution representing the quantization 'Y = ceiling(X)'", "concept": [ "distributions" ], "topics": [ "tfd_quantized" ] }, { "page": "tfd_relaxed_bernoulli", "title": "RelaxedBernoulli distribution with temperature and logits parameters", "concept": [ "distributions" ], "topics": [ "tfd_relaxed_bernoulli" ] }, { "page": "tfd_relaxed_one_hot_categorical", "title": "RelaxedOneHotCategorical distribution with temperature and logits", "concept": [ "distributions" ], "topics": [ "tfd_relaxed_one_hot_categorical" ] }, { "page": "tfd_sample", "title": "Generate samples of the specified shape.", "concept": [ "distribution_methods" ], "topics": [ "tfd_sample" ] }, { "page": "tfd_sample_distribution", "title": "Sample distribution via independent draws.", "concept": [ "distributions" ], "topics": [ "tfd_sample_distribution" ] }, { "page": "tfd_sinh_arcsinh", "title": "The SinhArcsinh transformation of a distribution on (-inf, inf)", "concept": [ "distributions" ], "topics": [ "tfd_sinh_arcsinh" ] }, { "page": "tfd_skellam", "title": "Skellam distribution.", "concept": [ "distributions" ], "topics": [ "tfd_skellam" ] }, { "page": "tfd_spherical_uniform", "title": "The uniform distribution over unit vectors on 'S^{n-1}'.", "concept": [ "distributions" ], "topics": [ "tfd_spherical_uniform" ] }, { "page": "tfd_stddev", "title": "Standard deviation.", "concept": [ "distribution_methods" ], "topics": [ "tfd_stddev" ] }, { "page": "tfd_student_t", "title": "Student's t-distribution", "concept": [ "distributions" ], "topics": [ "tfd_student_t" ] }, { "page": "tfd_student_t_process", "title": "Marginal distribution of a Student's T process at finitely many points", "concept": [ "distributions" ], "topics": [ "tfd_student_t_process" ] }, { "page": "tfd_survival_function", "title": "Survival function.", "concept": [ "distribution_methods" ], "topics": [ "tfd_survival_function" ] }, { "page": "tfd_transformed_distribution", "title": "A Transformed Distribution", "concept": [ "distributions" ], "topics": [ "tfd_transformed_distribution" ] }, { "page": "tfd_triangular", "title": "Triangular distribution with 'low', 'high' and 'peak' parameters", "concept": [ "distributions" ], "topics": [ "tfd_triangular" ] }, { "page": "tfd_truncated_cauchy", "title": "The Truncated Cauchy distribution.", "concept": [ "distributions" ], "topics": [ "tfd_truncated_cauchy" ] }, { "page": "tfd_truncated_normal", "title": "Truncated Normal distribution", "concept": [ "distributions" ], "topics": [ "tfd_truncated_normal" ] }, { "page": "tfd_uniform", "title": "Uniform distribution with 'low' and 'high' parameters", "concept": [ "distributions" ], "topics": [ "tfd_uniform" ] }, { "page": "tfd_variance", "title": "Variance.", "concept": [ "distribution_methods" ], "topics": [ "tfd_variance" ] }, { "page": "tfd_variational_gaussian_process", "title": "Posterior predictive of a variational Gaussian process", "concept": [ "distributions" ], "topics": [ "tfd_variational_gaussian_process" ] }, { "page": "tfd_vector_deterministic", "title": "Vector Deterministic Distribution", "topics": [ "tfd_vector_deterministic" ] }, { "page": "tfd_vector_diffeomixture", "title": "VectorDiffeomixture distribution", "concept": [ "distributions" ], "topics": [ "tfd_vector_diffeomixture" ] }, { "page": "tfd_vector_exponential_diag", "title": "The vectorization of the Exponential distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_vector_exponential_diag" ] }, { "page": "tfd_vector_exponential_linear_operator", "title": "The vectorization of the Exponential distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_vector_exponential_linear_operator" ] }, { "page": "tfd_vector_laplace_diag", "title": "The vectorization of the Laplace distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_vector_laplace_diag" ] }, { "page": "tfd_vector_laplace_linear_operator", "title": "The vectorization of the Laplace distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_vector_laplace_linear_operator" ] }, { "page": "tfd_vector_sinh_arcsinh_diag", "title": "The (diagonal) SinhArcsinh transformation of a distribution on 'R^k'", "concept": [ "distributions" ], "topics": [ "tfd_vector_sinh_arcsinh_diag" ] }, { "page": "tfd_von_mises", "title": "The von Mises distribution over angles", "concept": [ "distributions" ], "topics": [ "tfd_von_mises" ] }, { "page": "tfd_von_mises_fisher", "title": "The von Mises-Fisher distribution over unit vectors on 'S^{n-1}'", "concept": [ "distributions" ], "topics": [ "tfd_von_mises_fisher" ] }, { "page": "tfd_weibull", "title": "The Weibull distribution with 'concentration' and 'scale' parameters.", "concept": [ "distributions" ], "topics": [ "tfd_weibull" ] }, { "page": "tfd_wishart", "title": "The matrix Wishart distribution on positive definite matrices", "concept": [ "distributions" ], "topics": [ "tfd_wishart" ] }, { "page": "tfd_wishart_linear_operator", "title": "The matrix Wishart distribution on positive definite matrices", "concept": [ "distributions" ], "topics": [ "tfd_wishart_linear_operator" ] }, { "page": "tfd_wishart_tri_l", "title": "The matrix Wishart distribution parameterized with Cholesky factors.", "concept": [ "distributions" ], "topics": [ "tfd_wishart_tri_l" ] }, { "page": "tfd_zipf", "title": "Zipf distribution", "concept": [ "distributions" ], "topics": [ "tfd_zipf" ] }, { "page": "tfp", "title": "Handle to the 'tensorflow_probability' module", "topics": [ "tfp" ] }, { "page": "tfp_version", "title": "TensorFlow Probability Version", "topics": [ "tfp_version" ] }, { "page": "vi_amari_alpha", "title": "The Amari-alpha Csiszar-function in log-space", "concept": [ "vi-functions" ], "topics": [ "vi_amari_alpha" ] }, { "page": "vi_arithmetic_geometric", "title": "The Arithmetic-Geometric Csiszar-function in log-space", "concept": [ "vi-functions" ], "topics": [ "vi_arithmetic_geometric" ] }, { "page": "vi_chi_square", "title": "The chi-square Csiszar-function in log-space", "concept": [ "vi-functions" ], "topics": [ "vi_chi_square" ] }, { "page": "vi_csiszar_vimco", "title": "Use VIMCO to lower the variance of the gradient of csiszar_function(Avg(logu))", "concept": [ "vi-functions" ], "topics": [ "vi_csiszar_vimco" ] }, { "page": "vi_dual_csiszar_function", "title": "Calculates the dual Csiszar-function in log-space", "concept": [ "vi-functions" ], "topics": [ "vi_dual_csiszar_function" ] }, { "page": "vi_fit_surrogate_posterior", "title": "Fit a surrogate posterior to a target (unnormalized) log density", "concept": [ "vi-functions" ], "topics": [ "vi_fit_surrogate_posterior" ] }, { "page": "vi_jeffreys", "title": "The Jeffreys Csiszar-function in log-space", "concept": [ "vi-functions" ], "topics": [ "vi_jeffreys" ] }, { "page": 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