tfp.experimental.substrates.jax.experimental.inference_gym.targets.LogGaussianCoxProcess

Log-Gaussian Cox Process model.

Inherits From: BayesianModel

train_locations Float Tensor with shape [num_train_points, D]. Training set locations where counts were measured.
train_extents Float Tensor with shape [num_train_points]. Training set location extents, must be positive.
train_counts Integer Tensor with shape [num_train_points]. Training set counts, must be positive.
dtype Datatype to use for the model. Gaussian Process regression tends to require double precision.
name Python str name prefixed to Ops created by this class.
pretty_name A Python str. The pretty name of this model.

ValueError If the parallel arrays are not all of the same size.

default_event_space_bijector Bijector mapping the reals (R**n) to the event space of this model.
dtype The DType of Tensors handled by this model.
event_shape Shape of a single sample from as a TensorShape.

May be partially defined or unknown.

name Python str name prefixed to Ops created by this class.
sample_transformations A dictionary of names to SampleTransformations.

Child Classes

class SampleTransformation

Methods

log_likelihood

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Evaluates the log_likelihood at value.

prior_distribution

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The prior distribution over the model parameters.

unnormalized_log_prob

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The un-normalized log density of evaluated at a point.

This corresponds to the target distribution associated with the model, often its posterior.

Args
value A (nest of) Tensor to evaluate the log density at.
name Python str name prefixed to Ops created by this method.

Returns
unnormalized_log_prob A floating point Tensor.