pypomp.core.results.PompPFilterResult¶

class pypomp.core.results.PompPFilterResult(method: str, execution_time: float | None, key: Array, timestamp: Timestamp = <factory>, theta: PompParameters | None = None, logLiks: DataArray = <factory>, J: int = 0, reps: int = 1, thresh: float = 0.0, CLL_da: DataArray | None = None, ESS_da: DataArray | None = None, filter_mean: DataArray | None = None, prediction_mean: DataArray | None = None)[source]¶

Bases: PompBaseResult

Result from Pomp.pfilter() method.

Attributes

CLL_da: DataArray | None = None¶: Conditional log-likelihoods for each unit and time point. Dimensions: (“theta_idx”, “rep”, “time”)

ESS_da: DataArray | None = None¶: Effective Sample Size for each unit and time point. Dimensions: (“theta_idx”, “rep”, “time”)

J: int = 0¶: The number of particles used for filtering.

filter_mean: DataArray | None = None¶: The mean of the filtering distribution for each state variable. Dimensions: (“theta_idx”, “rep”, “state”, “time”)

prediction_mean: DataArray | None = None¶: The mean of the predictive distribution for each state variable. Dimensions: (“theta_idx”, “rep”, “state”, “time”)

reps: int = 1¶: The number of replicates for each parameter set.

theta: PompParameters | None = None¶: The parameter object used for the computation.

thresh: float = 0.0¶: The resampling threshold used by the filter.

logLiks: DataArray¶: Log-likelihoods for each parameter set and replicate. Dimensions: (“theta_idx”, “rep”)

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 pfilter result to DataFrame.
`traces`()	Return traces DataFrame for this pfilter result.