HSGPKwargs

class pymc_marketing.hsgp_kwargs.HSGPKwargs(**data)

HSGP keyword arguments for the time-varying prior.

See [1] and [2] for the theoretical background on the Hilbert Space Gaussian Process (HSGP). See , [6] for a practical guide through the method using code examples. See the HSGP class for more information on the Hilbert Space Gaussian Process in PyMC. We also recommend the following resources for a more practical introduction to HSGP: [3], [4], [5].

Parameters:

mint

Number of basis functions. Default is 200.

Lfloat, optional

Extent of basis functions. Set this to reflect the expected range of in+out-of-sample data (considering that time-indices are zero-centered).Default is X_mid * 2 (identical to c=2 in HSGP). By default it is None.

eta_lamfloat

Exponential prior for the variance. Default is 1.

ls_mufloat

Mean of the inverse gamma prior for the lengthscale. Default is 5.

ls_sigmafloat

Standard deviation of the inverse gamma prior for the lengthscale. Default is 5.

cov_funcCovariance, optional

Gaussian process Covariance function. By default it is None.

References

[1]

Solin, A., Sarkka, S. (2019) Hilbert Space Methods for Reduced-Rank Gaussian Process Regression.

[2]

Ruitort-Mayol, G., and Anderson, M., and Solin, A., and Vehtari, A. (2022). Practical Hilbert Space Approximate Bayesian Gaussian Processes for Probabilistic Programming.

[3]

PyMC Example Gallery: “Gaussian Processes: HSGP Reference & First Steps”.

[4]

PyMC Example Gallery: “Gaussian Processes: HSGP Advanced Usage”.

[5]

PyMC Example Gallery: “Baby Births Modelling with HSGPs”.

[6]

Orduz, J. “A Conceptual and Practical Introduction to Hilbert Space GPs Approximation Methods”.

Methods

HSGPKwargs.__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
HSGPKwargs.construct([_fields_set])
HSGPKwargs.copy(*[, include, exclude, ...])	Returns a copy of the model.
HSGPKwargs.dict(*[, include, exclude, ...])
HSGPKwargs.from_orm(obj)
HSGPKwargs.json(*[, include, exclude, ...])
HSGPKwargs.model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
HSGPKwargs.model_copy(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
HSGPKwargs.model_dump(*[, mode, include, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
HSGPKwargs.model_dump_json(*[, indent, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
HSGPKwargs.model_json_schema([by_alias, ...])	Generates a JSON schema for a model class.
HSGPKwargs.model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
HSGPKwargs.model_post_init(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
HSGPKwargs.model_rebuild(*[, force, ...])	Try to rebuild the pydantic-core schema for the model.
HSGPKwargs.model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
HSGPKwargs.model_validate_json(json_data, *)	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
HSGPKwargs.model_validate_strings(obj, *[, ...])	Validate the given object with string data against the Pydantic model.
HSGPKwargs.parse_file(path, *[, ...])
HSGPKwargs.parse_obj(obj)
HSGPKwargs.parse_raw(b, *[, content_type, ...])
HSGPKwargs.schema([by_alias, ref_template])
HSGPKwargs.schema_json(*[, by_alias, ...])
HSGPKwargs.update_forward_refs(**localns)
HSGPKwargs.validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
m
L
eta_lam
ls_mu
ls_sigma
cov_func