functional.fit_mcmc()

Fit a forecasting model with NUTS (Hamiltonian Monte Carlo).

Usage

Source

functional.fit_mcmc(
    rng_key,
    model,
    data,
    covariates,
    *,
    num_warmup=1000,
    num_samples=1000,
    num_chains=1,
    progress_bar=False
)

Parameters

rng_key: Array

PRNG key for inference.

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_warmup: int = 1000

Number of warmup steps.

num_samples: int = 1000

Number of posterior samples.

num_chains: int = 1

Number of MCMC chains.

progress_bar: bool = False

Whether to display the MCMC progress bar.

Returns

MCMCFit

The posterior samples.

Raises

ValueError

If data and covariates have different durations.