pcntoolkit.math_functions.prior#

Attributes#

`DEFAULT_PRIOR_ARGS`
`PM_DISTMAP`

Classes#

`BasePrior`	Helper class that provides a standard way to create an ABC using
`CenteredRandomPrior`	Helper class that provides a standard way to create an ABC using
`LinearPrior`	Helper class that provides a standard way to create an ABC using
`Prior`	Helper class that provides a standard way to create an ABC using
`RandomPrior`	Helper class that provides a standard way to create an ABC using

Functions#

`make_prior`(→ BasePrior)
`prior_from_args`(→ BasePrior)

Module Contents#

class BasePrior(name: str = 'theta', dims: Tuple[str, Ellipsis] | str | None = None, mapping: str = 'identity', mapping_params: tuple = None, **kwargs)#

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

__eq__(other: BasePrior)#

apply_mapping(x: Any) → Any#

compile(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) → Any#

classmethod from_dict(dict: BasePrior.from_dict.dict, version: str | None = None) → BasePrior#

abstractmethod set_name(name: str) → None#

to_dict()#

abstractmethod transfer(idata: arviz.InferenceData, **kwargs) → BasePrior#

abstractmethod update_data(model, X, be, be_maps, Y)#

property dims#

property has_random_effect: bool#

Abstractmethod:

mapping = 'identity'#

mapping_params = (0, 1)#

name = 'theta'#

sample_dims = ()#

class CenteredRandomPrior(mu: BasePrior | None = None, sigma: BasePrior | None = None, name: str = 'theta', dims: Tuple[str, Ellipsis] | str | None = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, **kwargs)#

Bases: BasePrior

Helper class that provides a standard way to create an ABC using inheritance.

classmethod from_dict(dict: CenteredRandomPrior.from_dict.dict, version: str | None = None) → CenteredRandomPrior#

set_name(name: str)#

to_dict()#

transfer(idata: arviz.InferenceData, **kwargs) → RandomPrior#

update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray)#

property dims#

property has_random_effect#

mu#

offsets#

sample_dims = ('observations',)#

scaled_offsets#

sigma#

sigmas#

class LinearPrior(slope: BasePrior | None = None, intercept: BasePrior | None = None, name: str = 'theta', dims: Tuple[str, Ellipsis] | str | None = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, basis_function: pcntoolkit.math_functions.basis_function.BasisFunction = LinearBasisFunction(), **kwargs)#

Bases: BasePrior

Helper class that provides a standard way to create an ABC using inheritance.

classmethod from_dict(dict: LinearPrior.from_dict.dict, version: str | None = None) → LinearPrior#

set_name(name)#

to_dict()#

transfer(idata: arviz.InferenceData, **kwargs) → LinearPrior#

update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray)#

basis_function#

property dims#

property has_random_effect#

intercept#

sample_dims = ('observations',)#

slope#

class Prior(name: str = 'theta', dims: Tuple[str, Ellipsis] | str | None = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, dist_name: str = 'Normal', dist_params: Tuple[float | int | list[float | int], Ellipsis] = None, **kwargs)#

Bases: BasePrior

Helper class that provides a standard way to create an ABC using inheritance.

classmethod from_dict(dict: Prior.from_dict.dict, version: str | None = None)#

set_name(name: str) → None#

to_dict()#

transfer(idata: arviz.InferenceData, **kwargs) → Prior#

update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray)#

dist_name = 'Normal'#

dist_params = (0, 10.0)#

property has_random_effect#

sample_dims = ()#

class RandomPrior(mu: BasePrior | None = None, sigma: BasePrior | None = None, name: str = 'theta', dims: Tuple[str, Ellipsis] | str | None = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, **kwargs)#

Bases: BasePrior

Helper class that provides a standard way to create an ABC using inheritance.

classmethod from_dict(dict: RandomPrior.from_dict.dict, version: str | None = None) → RandomPrior#

set_name(name: str)#

to_dict()#

transfer(idata: arviz.InferenceData, **kwargs) → RandomPrior#

update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray)#

property dims#

property has_random_effect#

mu#

offsets#

sample_dims = ('observations',)#

scaled_offsets#

sigma#

sigmas#

make_prior(name: str = 'theta', **kwargs) → BasePrior#

prior_from_args(name: str, args: Dict[str, Any], dims: Tuple[str, Ellipsis] | str | None = None) → BasePrior#

DEFAULT_PRIOR_ARGS#

PM_DISTMAP#