beanmachine.ppl.inference.predictive module

class beanmachine.ppl.inference.predictive.Predictive

Bases: object

Class for the posterior predictive distribution.

static empirical(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], samples: beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples, num_samples: Optional[int] = 1) → beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples

Samples from the empirical (marginal) distribution of the queried variables.

Parameters

• queries – list of random_variable’s to be sampled.

• samples – MonteCarloSamples of the distribution.

• num_samples – Number of samples to sample (with replacement). Defaults to 1.

Returns

MonteCarloSamples object containing the sampled random variables.

static simulate(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], posterior: Optional[Union[beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples, Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor]]] = None, num_samples: Optional[int] = None, vectorized: Optional[bool] = False, progress_bar: Optional[bool] = True) → beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples

Generates predictives from a generative model.

For example:

obs_queries = [likelihood(i) for i in range(10))]
posterior = SinglesiteHamiltonianMonteCarlo(10, 0.1).infer(...)
# generates one sample per world (same shape as `posterior` samples)
predictives = simulate(obs_queries, posterior=posterior)

To generate prior predictives:

queries = [prior(), likelihood()]  # specify the full generative model
# Monte carlo samples of shape (num_samples, sample_shape)
predictives = simulate(queries, num_samples=1000)

Parameters

• query – list of random_variable’s corresponding to the observations.

• posterior – Optional MonteCarloSamples or RVDict of the latent variables.

• num_samples – Number of prior predictive samples, defaults to 1. Should not be specified if posterior is specified.

Returns

MonteCarloSamples of the generated predictives.

beanmachine.ppl.inference.predictive.empirical(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], samples: beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples, num_samples: Optional[int] = 1) → beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples

Samples from the empirical (marginal) distribution of the queried variables.

Parameters

• queries – list of random_variable’s to be sampled.

• samples – MonteCarloSamples of the distribution.

• num_samples – Number of samples to sample (with replacement). Defaults to 1.

Returns

MonteCarloSamples object containing the sampled random variables.

beanmachine.ppl.inference.predictive.simulate(queries: List[beanmachine.ppl.model.rv_identifier.RVIdentifier], posterior: Optional[Union[beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples, Dict[beanmachine.ppl.model.rv_identifier.RVIdentifier, torch.Tensor]]] = None, num_samples: Optional[int] = None, vectorized: Optional[bool] = False, progress_bar: Optional[bool] = True) → beanmachine.ppl.inference.monte_carlo_samples.MonteCarloSamples

Generates predictives from a generative model.

For example:

obs_queries = [likelihood(i) for i in range(10))]
posterior = SinglesiteHamiltonianMonteCarlo(10, 0.1).infer(...)
# generates one sample per world (same shape as `posterior` samples)
predictives = simulate(obs_queries, posterior=posterior)

To generate prior predictives:

queries = [prior(), likelihood()]  # specify the full generative model
# Monte carlo samples of shape (num_samples, sample_shape)
predictives = simulate(queries, num_samples=1000)

Parameters

• query – list of random_variable’s corresponding to the observations.

• posterior – Optional MonteCarloSamples or RVDict of the latent variables.

• num_samples – Number of prior predictive samples, defaults to 1. Should not be specified if posterior is specified.

Returns

MonteCarloSamples of the generated predictives.