pcntoolkit.math_functions.likelihood

Classes

`BetaLikelihood`	Helper class that provides a standard way to create an ABC using
`Likelihood`	Helper class that provides a standard way to create an ABC using
`NormalLikelihood`	Helper class that provides a standard way to create an ABC using
`SHASHbLikelihood`	Helper class that provides a standard way to create an ABC using
`SHASHo2Likelihood`	Helper class that provides a standard way to create an ABC using
`SHASHoLikelihood`	Helper class that provides a standard way to create an ABC using

Functions

get_default_normal_likelihood(→ NormalLikelihood)

Module Contents

class BetaLikelihood(alpha: pcntoolkit.math_functions.prior.BasePrior, beta: pcntoolkit.math_functions.prior.BasePrior)

Bases: Likelihood

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

backward(*args, **kwargs)

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

forward(*args, **kwargs)

get_var_names() → List[str]

has_random_effect() → bool

to_dict() → Dict[str, Any]

transfer(idata: arviz.InferenceData, **kwargs) → BetaLikelihood

yhat(*args, **kwargs)

alpha

beta

class Likelihood(name: str)

Bases: abc.ABC

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

abstractmethod backward(*args, **kwargs)

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

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

create_model_with_data(X, be, be_maps, Y) → pymc.Model

abstractmethod forward(*args, **kwargs)

static from_args(args: Dict[str, Any]) → Likelihood

static from_dict(dct: Dict[str, Any], version: str | None = None) → Likelihood

abstractmethod has_random_effect() → bool

abstractmethod to_dict() → Dict[str, Any]

abstractmethod transfer(idata: arviz.InferenceData, **kwargs) → Likelihood

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

abstractmethod yhat(*args, **kwargs)

name

class NormalLikelihood(mu: pcntoolkit.math_functions.prior.BasePrior, sigma: pcntoolkit.math_functions.prior.BasePrior)

Bases: Likelihood

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

backward(*args, **kwargs)

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

forward(*args, **kwargs)

has_random_effect() → bool

to_dict() → Dict[str, Any]

transfer(idata: arviz.InferenceData, **kwargs) → Likelihood

yhat(*args, **kwargs)

mu

sigma

class SHASHbLikelihood(mu: pcntoolkit.math_functions.prior.BasePrior, sigma: pcntoolkit.math_functions.prior.BasePrior, epsilon: pcntoolkit.math_functions.prior.BasePrior, delta: pcntoolkit.math_functions.prior.BasePrior)

Bases: Likelihood

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

backward(*args, **kwargs)

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