bayespecon.models.flow_panel.PoissonFlowPanel¶

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

Non-spatial Bayesian OD-flow Poisson gravity model for balanced panel data.

Panel analogue of PoissonFlow and count analogue of OLSFlowPanel. Models stacked panel flow counts with a log-linear gravity mean and no spatial-lag terms,

\[y_{ij,t} \sim \operatorname{Poisson}(\lambda_{ij,t}), \qquad \log \boldsymbol{\lambda}_{t} = X_{t}\beta,\]

on a balanced panel of \(T\) periods. Provided as the canonical aspatial count baseline for panel flow data.

Parameters:¶

y¶: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
G¶: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
X¶: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
T¶: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
col_names: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
k: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
priors: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.
symmetric_xo_xd: See FlowPanelModel. G is required for API symmetry but the spatial weights are not used in estimation.

Notes

Currently supports pooled panels only (model=0). Within-transformed fixed-effects panels are not valid for Poisson counts (they break the non-negative integer support), matching the restriction enforced by PoissonSARFlowPanel.

The priors dict supports beta_mu and beta_sigma; spatial keys (rho_*), sigma_sigma, and the robust flag are ignored (Poisson has no scale parameter to robustify).

__init__(y, G, X, T, **kwargs)[source]¶

Methods

`__init__`(y, G, X, T, **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 flow counts for the panel Poisson gravity model.
`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 flow counts for the panel Poisson gravity model.

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.