pypomp.core.results.PompMIFResult

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

Bases: PompEstimationTracesMixin, PompBaseResult

Result from Pomp.mif() method.

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.

Attributes

J	The number of particles used for filtering.
M	The number of iterations performed.
a	The cooling fraction used.
n_monitors	The number of particle filters used to estimate log-likelihoods at each iteration.
rw_sd	The random walk standard deviations for parameter perturbation.
thresh	The resampling threshold used.
traces_da	Parameter traces and log-likelihoods across iterations.
theta	The list of parameter sets used for the computation.
method	The name of the method that produced this result (e.g., 'pfilter', 'mif').
execution_time	Total execution time in seconds.
key	The JAX random key used for this execution.
timestamp	The date and time when the result was created.