## contrib.blackjax.fit_pathfinder()


Fit a forecasting model with BlackJAX Pathfinder variational inference.


Usage

``` python
contrib.blackjax.fit_pathfinder(
    rng_key,
    model,
    data,
    covariates,
    *,
    num_elbo_samples=200,
    ftol=1e-05,
    maxiter=30
)
```


PRNG: `rng_key` is split into a model-initialization stream and a Pathfinder-approximation stream.


## Parameters


`rng_key: Array`  
PRNG key for initialization and the Pathfinder run.

`model: ForecastModel`  
The forecasting model callable (OOP instance or functional model).

`data: Array`  
In-sample data with time at axis `-2`.

`covariates: Array`  
Covariates with time at axis `-2` and the same duration as `data`.

`num_elbo_samples: int = ``200`  
Number of Monte Carlo samples used to estimate the ELBO along the L-BFGS optimization path.

`ftol: float = ``1e-05`  
L-BFGS relative function-value tolerance (convergence criterion).

`maxiter: int = ``30`  
Maximum number of L-BFGS iterations (blackjax's default is `30`). Models with per-step time latents typically need far more: the default suits tens of parameters, while a few hundred parameters (e.g. a 400-step random walk) converge around `1_000`.


## Returns


`PathfinderFit`  
The fitted variational approximation.


## Notes

Runs with `_stable_bfgs_sample()` patched into blackjax (via `_ensure_stable_bfgs_sample()`): upstream's `bfgs_sample` underflows its log-determinant beyond a few hundred parameters, which floors every path ELBO at `-inf` and makes the returned state garbage.