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]#

forward(*args, **kwargs)#

get_var_names() → List[str]#

has_random_effect() → bool#

to_dict() → Dict[str, Any]#

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

yhat(*args, **kwargs)#

delta#

epsilon#

mu#

sigma#

class SHASHo2Likelihood(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)#

forward(*args, **kwargs)#

get_var_names() → List[str]#

has_random_effect() → bool#

to_dict() → Dict[str, Any]#

delta#

epsilon#

mu#

sigma#

class SHASHoLikelihood(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)#

forward(*args, **kwargs)#

get_var_names() → List[str]#

has_random_effect() → bool#

to_dict() → Dict[str, Any]#

delta#

epsilon#

mu#

sigma#

get_default_normal_likelihood() → NormalLikelihood#