pypomp.core.results.PompMIFResult

class pypomp.core.results.PompMIFResult(method: str, execution_time: float | None, key: Array, timestamp: Timestamp = <factory>, theta: PompParameters | None = None, traces_da: DataArray = <factory>, J: int = 0, M: int = 0, rw_sd: RWSigma | None = None, thresh: float = 0.0, n_monitors: int = 0)

Bases: PompEstimationTracesMixin, PompBaseResult

Result from Pomp.mif() method.

Attributes

J: int = 0

The number of particles used for filtering.

M: int = 0

The number of iterations performed.

n_monitors: int = 0

The number of particle filters used to estimate log-likelihoods at each iteration.

rw_sd: RWSigma | None = None

The random walk standard deviations for parameter perturbation.

theta: PompParameters | None = None

The parameter object used for the computation.

thresh: float = 0.0

The resampling threshold used.

traces_da: DataArray

Log-likelihood and parameter traces across iterations. Dimensions: (“theta_idx”, “iteration”, “variable”)

method: str

The name of the method that produced this result (e.g., ‘pfilter’, ‘mif’).

execution_time: float | None

Total execution time in seconds.

key: jax.Array

The JAX random key used for this execution.

timestamp: pd.Timestamp

The date and time when the result was created.

Methods

CLL([average])	Return conditional log-likelihoods as a DataFrame.
ESS([average])	Return Effective Sample Size as a DataFrame.
__init__(method, execution_time, key, ...)
merge(*results)	Merge multiple result objects of the same type.
print_summary([n])	Print a summary of this result.
to_dataframe([ignore_nan])	Convert result to DataFrame using traces_da.
traces()	Return traces DataFrame using traces_da.