Family

families.Family(self, name, likelihood, link)

A specification of model family

Parameters

Name	Type	Description	Default
name	str	The name of the family. It can be any string.	required
likelihood	Likelihood	A bambi.families.Likelihood instance specifying the model likelihood function.	required
link	Union[str, Dict[str, Union[str, Link]]]	The link function that’s used for every parameter in the likelihood function. Keys are the names of the parameters and values are the link functions. These can be a str with a name or a bambi.families.Link instance. The link function transforms the linear predictors.	required

Examples

>>> import bambi as bmb

Replicate the Gaussian built-in family.

>>> sigma_prior = bmb.Prior("HalfNormal", sigma=1)
>>> likelihood = bmb.Likelihood("Gaussian", params=["mu", "sigma"], parent="mu")
>>> family = bmb.Family("gaussian", likelihood, "identity")
>>> bmb.Model("y ~ x", data, family=family, priors={"sigma": sigma_prior})

Replicate the Bernoulli built-in family.

>>> likelihood = bmb.Likelihood("Bernoulli", parent="p")
>>> family = bmb.Family("bernoulli", likelihood, "logit")
>>> bmb.Model("y ~ x", data, family=family)

Attributes

Name	Description
auxiliary_parameters	Get names of auxiliary parameters

Methods

Name	Description
log_likelihood	Evaluate the model log-likelihood
posterior_predictive	Get draws from the posterior predictive distribution
set_default_priors	Set default priors for non-parent parameters

log_likelihood

Family.log_likelihood(self, model, posterior, data, **kwargs)

Evaluate the model log-likelihood

This method uses pm.logp().

Parameters

Name	Type	Description	Default
model	bambi.Model	The model	required
posterior	xr.Dataset	The xarray dataset that contains the draws for all the parameters in the posterior. It must contain the parameters that are needed in the distribution of the response, or the parameters that allow to derive them.	required
kwargs		Parameters that are used to get draws but do not appear in the posterior object or other configuration parameters. For instance, the ‘n’ in binomial models and multinomial models.	{}

Returns

Type	Description
xr.DataArray	A data array with the value of the log-likelihood for each chain, draw, and value of the response variable.

posterior_predictive

Family.posterior_predictive(self, model, posterior, **kwargs)

Get draws from the posterior predictive distribution

This function works for almost all the families. It grabs the draws for the parameters needed in the response distribution, and then gets samples from the posterior predictive distribution using pm.draw(). It won’t work when the response distribution requires parameters that are not available in posterior.

Parameters

Name	Type	Description	Default
model	bambi.Model	The model	required
posterior	xr.Dataset	The xarray dataset that contains the draws for all the parameters in the posterior. It must contain the parameters that are needed in the distribution of the response, or the parameters that allow to derive them.	required
kwargs		Parameters that are used to get draws but do not appear in the posterior object or other configuration parameters. For instance, the ‘n’ in binomial models and multinomial models.	{}

Returns

Type	Description
xr.DataArray	A data array with the draws from the posterior predictive distribution.

set_default_priors

Family.set_default_priors(self, priors)

Set default priors for non-parent parameters

Parameters

Name	Type	Description	Default
priors	dict	The keys are the names of non-parent parameters and the values are their default priors.	required