bayespecon.models.flow_panel.PoissonSARFlowSeparablePanel¶

class bayespecon.models.flow_panel.PoissonSARFlowSeparablePanel(y, G, X, **kwargs)[source]¶

Panel separable Poisson spatial-lag flow model.

The panel counts satisfy

\[y_{ij,t} \sim \operatorname{Poisson}(\lambda_{ij,t}), \qquad \log \boldsymbol{\lambda}_t = A(\rho_d, \rho_o)^{-1} X_t \beta,\]

with the separability restriction \(\rho_w = -\rho_d \rho_o\) and

\[A(\rho_d, \rho_o) = I_N - \rho_d W_d - \rho_o W_o + \rho_d \rho_o W_w.\]

Notes

This class currently supports pooled panels only (model=0). The separability restriction enables the Kronecker-factorized log-determinant used in estimation.

__init__(y, G, X, **kwargs)[source]¶

Methods

`__init__`(y, G, X, **kwargs)
`fit`([draws, tune, chains, target_accept, ...])	Draw samples from the posterior.
`fit_approx`([draws, n, method, random_seed, ...])	Fit a variational approximation and return posterior draws.
`posterior_predictive`([n_draws, random_seed])	Draw posterior-predictive samples `y_rep` for the full panel stack.
`spatial_effects`([draws, ...])	Summarise posterior origin/destination/intra/network/total effects.
`summary`([var_names])	Return posterior summary table via ArviZ.

Attributes

`approximation`	Return the most recent PyMC variational approximation, if any.
`inference_data`	Return posterior draws from the most recent fit.
`pymc_model`	Return the most recently built PyMC model.

property approximation[source]¶: Return the most recent PyMC variational approximation, if any.

fit(draws=2000, tune=1000, chains=4, target_accept=0.9, random_seed=None, store_lambda=False, idata_kwargs=None, **sample_kwargs)[source]¶: Draw samples from the posterior.

fit_approx(draws=2000, n=10000, method='advi', random_seed=None, store_lambda=False, compute_log_likelihood=True, **fit_kwargs)[source]¶: Fit a variational approximation and return posterior draws.

property inference_data : arviz.data.inference_data.InferenceData | None[source]¶: Return posterior draws from the most recent fit.

posterior_predictive(n_draws=None, random_seed=None)[source]¶

Draw posterior-predictive samples y_rep for the full panel stack.

Parameters:¶

n_draws : int, optional¶: Number of posterior draws to use. Defaults to all.
random_seed : int, optional¶: Seed for the noise/Poisson sampler.

Returns:¶

Array of shape (n_draws, N_flow * T) with posterior-predictive flows in time-first stacked order.

Return type:¶

np.ndarray

property pymc_model : pymc.model.core.Model | None[source]¶: Return the most recently built PyMC model.

spatial_effects(draws=None, return_posterior_samples=False, ci=0.95, mode='auto')[source]¶

Summarise posterior origin/destination/intra/network/total effects.

See bayespecon.models.flow.FlowModel.spatial_effects() for the mode semantics (auto / combined / separate destination-origin sides per Thomas-Agnan & LeSage 2014, §83.5.2).

summary(var_names=None, **kwargs)[source]¶: Return posterior summary table via ArviZ.