Inference Utilities¶
predictive¶
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predictive
(rng, model, posterior_samples, return_sites=None, *args, **kwargs)[source]¶ Run model by sampling latent parameters from posterior_samples, and return values at sample sites from the forward run. By default, only sites not contained in posterior_samples are returned. This can be modified by changing the return_sites keyword argument.
Warning
The interface for the predictive function is experimental, and might change in the future.
Parameters: - rng (jax.random.PRNGKey) – seed to draw samples
- model – Python callable containing Pyro primitives.
- posterior_samples (dict) – dictionary of samples from the posterior.
- return_sites (list) – sites to return; by default only sample sites not present in posterior_samples are returned.
- args – model arguments.
- kwargs – model kwargs.
Returns: dict of samples from the predictive distribution.
log_density¶
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log_density
(model, model_args, model_kwargs, params, skip_dist_transforms=False)[source]¶ Computes log of joint density for the model given latent values
params
.Parameters: - model – Python callable containing NumPyro primitives.
- model_args (tuple) – args provided to the model.
- model_kwargs` (dict) – kwargs provided to the model.
- params (dict) – dictionary of current parameter values keyed by site name.
- skip_dist_transforms (bool) – whether to compute log probability of a site (if its prior is a transformed distribution) in its base distribution domain.
Returns: log of joint density and a corresponding model trace
transform_fn¶
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transform_fn
(transforms, params, invert=False)[source]¶ Callable that applies a transformation from the transforms dict to values in the params dict and returns the transformed values keyed on the same names.
Parameters: - transforms – Dictionary of transforms keyed by names. Names in transforms and params should align.
- params – Dictionary of arrays keyed by names.
- invert – Whether to apply the inverse of the transforms.
Returns: dict of transformed params.
constrain_fn¶
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constrain_fn
(model, model_args, model_kwargs, transforms, params)[source]¶ Gets value at each latent site in model given unconstrained parameters params. The transforms is used to transform these unconstrained parameters to base values of the corresponding priors in model. If a prior is a transformed distribution, the corresponding base value lies in the support of base distribution. Otherwise, the base value lies in the support of the distribution.
Parameters: - model – a callable containing NumPyro primitives.
- model_args (tuple) – args provided to the model.
- model_kwargs` (dict) – kwargs provided to the model.
- transforms (dict) – dictionary of transforms keyed by names. Names in transforms and params should align.
- params (dict) – dictionary of unconstrained values keyed by site names.
Returns: dict of transformed params.
potential_energy¶
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potential_energy
(model, model_args, model_kwargs, inv_transforms, params)[source]¶ Makes a callable which computes potential energy of a model given unconstrained params. The inv_transforms is used to transform these unconstrained parameters to base values of the corresponding priors in model. If a prior is a transformed distribution, the corresponding base value lies in the support of base distribution. Otherwise, the base value lies in the support of the distribution.
Parameters: Returns: a callable that computes potential energy given unconstrained parameters.
init_to_median¶
init_to_uniform¶
init_to_feasible¶
find_valid_initial_params¶
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find_valid_initial_params
(rng, model, *model_args, init_strategy=<function init_to_uniform>, param_as_improper=False, prototype_params=None, **model_kwargs)[source]¶ Given a model with Pyro primitives, returns an initial valid unconstrained parameters. This function also returns an is_valid flag to say whether the initial parameters are valid.
Parameters: - rng (jax.random.PRNGKey) – random number generator seed to
sample from the prior. The returned init_params will have the
batch shape
rng.shape[:-1]
. - model – Python callable containing Pyro primitives.
- *model_args – args provided to the model.
- init_strategy (callable) – a per-site initialization function.
- param_as_improper (bool) – a flag to decide whether to consider sites with param statement as sites with improper priors.
- **model_kwargs – kwargs provided to the model.
Returns: tuple of (init_params, is_valid).
- rng (jax.random.PRNGKey) – random number generator seed to
sample from the prior. The returned init_params will have the
batch shape