multiple_inference.bayes.base#

Base classes for Bayesian analysis.

Classes

BayesBase(mean, cov[, X, endog_names, ...])

Mixin for Bayesian models.

BayesResults(*args[, n_samples])

Results of Bayesian analysis.
class multiple_inference.bayes.base.BayesBase(mean: Sequence[float], cov: ndarray, X: Optional[ndarray] = None, endog_names: Optional[str] = None, exog_names: Optional[Sequence[str]] = None, columns: Optional[Union[Sequence[int], Sequence[str], Sequence[bool]]] = None, sort: bool = False, random_state: int = 0)[source]#

Mixin for Bayesian models.

Subclasses multiple_inference.base.ModelBase.

get_joint_distribution(columns: Optional[Union[Sequence[int], Sequence[str], Sequence[bool]]] = None)[source]#

Get the joint posterior distribution.

Parameters

columns (ColumnsType, optional) – Selected columns. Defaults to None.

Returns

Joint distribution.

Return type

rv_like

get_joint_prior(columns: Optional[Union[Sequence[int], Sequence[str], Sequence[bool]]] = None)[source]#

Get the joint prior distribution.

Parameters

columns (ColumnsType, optional) – Selected columns. Defaults to None.

Returns

Joint distribution.

Return type

rv_like

get_marginal_distribution(column: Union[str, int]) → rv_continuous[source]#

Get the marginal posterior distribution of column.

Parameters

column (ColumnType) – Name or index of the parameter of interest.

Returns

Posterior distribution.

Return type

rv_continuous

get_marginal_prior(column: Union[str, int]) → rv_continuous[source]#

Get the marginal prior distribution of column.

Parameters

column (ColumnType) – Name or index of the parameter of interest.

Returns

Prior distribution

Return type

rv_continuous

class multiple_inference.bayes.base.BayesResults(*args: Any, n_samples: int = 10000, **kwargs: Any)[source]#

Results of Bayesian analysis.

Inherits from multiple_inference.base.ResultsBase.

Parameters
distributions#

Marginal posterior distributions.

Type

List[scipy.stats.norm]

multivariate_distribution#

Joint posterior distribution.

Type

scipy.stats.multivariate_normal

rank_df#

(n, n) dataframe of probabilities that column i has rank j.

Type

pd.DataFrame

expected_wasserstein_distance(mean: Optional[Sequence[float]] = None, cov: Optional[ndarray] = None, **kwargs: Any) → float[source]#

Compute the Wasserstein distance metric.

This method estimates the Wasserstein distance between the observed distribution (a joint normal characterized by mean and cov) and the distribution you would expect to observe according to this model.

Parameters

  • mean (Numeric1DArray, optional) – (# params,) array of sample conventionally estimated means. If None, use the model’s estimated means. Defaults to None.

  • cov (np.ndarray, optional) – (# params, # params) covaraince matrix for conventionally estimated means. If None, use the model’s estimated covariance matrix. Defaults to None.

  • **kwargs (Any) – Keyword arguments for scipy.stats.wasserstein_distance.

Returns

Expected Wasserstein distance.

Return type

float

likelihood(mean: Optional[Sequence[float]] = None, cov: Optional[ndarray] = None) → float[source]#
Parameters

  • mean (Numeric1DArray, optional) – (# params,) array of sample conventionally estimated means. If None, use the model’s estimated means. Defaults to None.

  • cov (np.ndarray, optional) – (# params, # params) covaraince matrix for conventionally estimated means. If None, use the model’s estimated covariance matrix. Defaults to None.

Returns

Likelihood.

Return type

float

line_plot(column: Optional[Union[int, str]] = None, alpha: float = 0.05, title: Optional[str] = None, yname: Optional[str] = None, ax=None)[source]#

Create a line plot of the prior, conventional, and posterior estimates.

Parameters

  • column (ColumnType, optional) – Selected parameter. Defaults to None.

  • alpha (float, optional) – Sets the plot width. 0 is as wide as possible, 1 is as narrow as possible. Defaults to .05.

  • title (str, optional) – Plot title. Defaults to None.

  • yname (str, optional) – Name of the dependent variable. Defaults to None.

  • ax (AxesSubplot, optional) – Axis to write on.

Returns

Plot.

Return type

AxesSubplot

rank_matrix_plot(title: Optional[str] = None, **kwargs: Any)[source]#

Plot a heatmap of the rank matrix.

Parameters

  • title (str, optional) – Plot title. Defaults to None.

  • **kwargs (Any) – Passed to sns.heatmap.

Returns

Heatmap.

Return type

AxesSubplot

reconstruction_point_plot(yname: Optional[str] = None, xname: Optional[Sequence[str]] = None, title: Optional[str] = None, alpha: float = 0.05, ax=None)[source]#

Create point plot of the reconstructed sample means.

Plots the distribution of sample means you would expect to see if this model were correct.

Parameters

  • yname (str, optional) – Name of the endogenous variable. Defaults to None.

  • xname (Sequence[str], optional) – Names of x-ticks. Defaults to None.

  • title (str, optional) – Plot title. Defaults to None.

  • alpha – (float, optional): Plot the 1-alpha CI. Defaults to 0.05.

  • ax – (AxesSubplot, optional): Axis to write on.

Returns

Plot.

Return type

plt.axes._subplots.AxesSubplot