pypomp.functional.pfilter¶
- pypomp.functional.pfilter(struct: PompStruct, thetas_array: Array, J: int, thresh: float, keys: Array, CLL: bool = False, ESS: bool = False, filter_mean: bool = False, prediction_mean: bool = False) dict[str, Array][source]¶
This is a pure functional implementation of the particle filter, intended for users who need to compose it within custom JAX loops or higher-order functions. For a more user-friendly (but non-functional) interface, see
pypomp.core.pomp.Pomp.pfilter().This implementation leverages JAX to efficiently vectorize the algorithm across multiple parameter sets simultaneously.
- Parameters:
struct (PompStruct) – The compiled structural representation of the POMP model.
thetas_array (jax.Array) – Array of initial parameters. Shape (n_reps, n_params).
J (int) – Number of particles.
thresh (float) – Resampling threshold.
keys (jax.Array) – Random keys. Shape (n_reps, reps, …).
CLL (bool) – Compute conditional log-likelihoods.
ESS (bool) – Compute effective sample size.
filter_mean (bool) – Compute filtered mean.
prediction_mean (bool) – Compute prediction mean.
- Returns:
A dictionary containing the results of the particle filter. The following entries are always present: - logLik: The log-likelihood estimate. The following entries are present if their corresponding flags are set to True: - CLL: Conditional log-likelihoods at each time point. - ESS: Effective sample size at each time point. - filter_mean: Filtered state means at each time point. - prediction_mean: Predicted state means at each time point.
- Return type: