bayespecon.models.flow.SEMFlowSeparable¶

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

Bayesian separable spatial-error flow model with \(\lambda_w = -\lambda_d \lambda_o\).

Spatial-error analogue of SARFlowSeparable. The separability constraint reduces \(\log|B|\) to the eigenvalue / Chebyshev factored form

\[\log|B| = n \log|I_n - \lambda_d W| + n \log|I_n - \lambda_o W|\]

enabling \(O(n)\) log-determinant evaluation per draw. All other properties (no \(X\)-mediated spillovers, closed-form effects, etc.) are identical to SEMFlow.

Parameters:¶

y¶: See FlowModel.
G¶: See FlowModel.
X¶: See FlowModel.
col_names: See FlowModel.
k: See FlowModel.
priors: See FlowModel.
miter: See FlowModel.
titer: See FlowModel.
trace_riter: See FlowModel.
trace_seed: See FlowModel.
logdet_method : {"eigenvalue", "chebyshev", "mc_poly", "separable"}, default "eigenvalue": Same options as SARFlowSeparable. "separable" is an alias for "eigenvalue".

__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`.
`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 ArviZ InferenceData from the most recent fit, or None.
`pymc_model`	Return the PyMC model used for the most recent fit, or None.

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.

Parameters:¶

draws : int, default 2000¶: Number of posterior samples per chain (after tuning).
tune : int, default 1000¶: Number of tuning (warm-up) steps per chain.
chains : int, default 4¶: Number of parallel chains.
target_accept : float, default 0.9¶: Target acceptance rate for NUTS.
random_seed : int, optional¶: Seed for reproducibility.
store_lambda : bool, default False¶: If True, include the high-dimensional fitted mean lambda in the stored posterior. Leaving this False reduces memory and conversion overhead for Poisson flow models.
idata_kwargs : dict, optional¶: Forwarded to pm.sample. Defaults to {"log_likelihood": True} so that az.loo / az.waic / az.compare work out of the box; for SAR flow variants the captured Gaussian log-likelihood is post-processed to add the Jacobian contribution from log|I_N - rho_d W_d - rho_o W_o - rho_w W_w|.
**sample_kwargs¶: Additional keyword arguments forwarded to pm.sample.

Return type:¶

arviz.InferenceData

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.

Parameters:¶

draws : int, default 2000¶: Number of samples to draw from the fitted approximation.
n : int, default 10000¶: Number of optimisation iterations for pm.fit.
method : {"advi", "fullrank_advi"}, default "advi"¶: Variational inference family to fit.
random_seed : int, optional¶: Seed for optimisation and posterior sampling.
store_lambda : bool, default False¶: If True, keep the high-dimensional fitted mean lambda in the posterior draws.
compute_log_likelihood : bool, default True¶: If True, compute pointwise log-likelihood after sampling and attach to the InferenceData (with Jacobian correction for SAR flow variants), enabling az.loo / az.waic.
**fit_kwargs¶: Additional keyword arguments forwarded to pm.fit.

property inference_data : arviz.data.inference_data.InferenceData | None[source]¶: Return ArviZ InferenceData from the most recent fit, or None.

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

Draw posterior-predictive samples y_rep.

For each (subsampled) posterior draw, simulates a new flow vector y_rep from the implied data-generating process by solving the sparse system A(rho) y_rep = X β + ε (Gaussian) or y_rep ~ Poisson(exp(A^{-1} X β)) (Poisson variants).

Parameters:¶

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

Returns:¶

Array of shape (n_draws, N) with posterior-predictive flows.

Return type:¶

np.ndarray

property pymc_model : pymc.model.core.Model | None[source]¶: Return the PyMC model used for the most recent fit, or None.

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

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

Wraps _compute_spatial_effects_posterior() to produce a tidy DataFrame indexed by predictor with posterior means, credible-interval bounds, and Bayesian p-values for each effect type (origin, destination, intra, network, total). Following Thomas-Agnan & LeSage (2014, §83.5.2), when destination and origin design blocks differ the decomposition is reported separately for shocks applied to each side.

Parameters:¶

draws : int, optional¶: Maximum number of posterior draws to use. Defaults to all.
return_posterior_samples : bool, default False¶: If True, also return the underlying posterior-draw arrays.
ci : float, default 0.95¶: Credible-interval coverage.
mode : {"auto", "combined", "separate"}, default "auto"¶: Controls whether destination- and origin-side effects are summed or reported separately. "auto" collapses to combined when the destination and origin design blocks are identical (self._symmetric_xo_xd) and reports both sides otherwise. "combined" always sums; "separate" always reports both.

Returns:¶

Long-format summary indexed by (predictor, side, effect) where side is one of "combined", "dest", "orig".

Return type:¶

pandas.DataFrame, or (DataFrame, dict)

summary(var_names=None, **kwargs)[source]¶

Return posterior summary table via ArviZ.

Parameters:¶

var_names : list, optional¶: Variable names to include. Defaults to all parameters.
**kwargs¶: Additional keyword arguments forwarded to az.summary.

Return type:¶

pandas.DataFrame