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TensorFlow Probability experimental distributions package.
Modules
marginal_fns module: Experimental functions to use as marginals for GaussianProcess(es).
Classes
class ImportanceResample: Models the distribution of finitely many importance-reweighted samples.
class IncrementLogProb: A distribution representing an unnormalized measure on a singleton set.
class JointDistributionPinned: A wrapper class for JointDistribution which pins, e.g., the evidence.
class MultiTaskGaussianProcess: Marginal distribution of a Multitask GP at finitely many points.
class MultiTaskGaussianProcessRegressionModel: Posterior predictive in a conjugate Multi-task GP regression model.
class MultivariateNormalPrecisionFactorLinearOperator: A multivariate normal on R^k, parametrized by a precision factor.
Functions
inflated_factory(...): Create Inflated subclasses for specific distributions and positions.
log_prob_ratio(...): Computes p.log_prob(x) - q.log_prob(y), numerically stably.