LotkaVolterra

class bayesflow.simulators.LotkaVolterra(X0: int = 30, Y0: int = 1, T: int | None = 20, subsample: int = 10, flatten: bool = True, obs_noise: float = 0.1, dt: float = None, rng: Generator = None)

Bases: BenchmarkSimulator

Lotka Volterra simulated benchmark. See: https://arxiv.org/pdf/2101.04653.pdf, Task T.10

Parameters:

X0: int, optional, default: 30

Initial number of prey species.

Y0: int, optional, default: 1

Initial number of predator species.

T: int, optional, default: 20

The duration (time horizon) of the simulation.

subsample: int or None, optional, default: 10

The number of evenly spaced time points to return. If None, no subsampling will be performed and all T timepoints will be returned.

flatten: bool, optional, default: True

A flag to indicate whether a 1D (flatten=True) or 2D (flatten=False) representation of the simulated data is returned.

obs_noise: float, optional, default: 0.1

The standard deviation of the log-normal likelihood.

rng: np.random.Generator or None, optional, default: None

An optional random number generator to use.

prior()

Generates a random draw from a 4-dimensional (independent) lognormal prior which represents the four contact parameters of the Lotka-Volterra model.

Returns:

paramsnp.ndarray of shape (4, )

A single draw from the 4-dimensional prior.

__call__(**kwargs) → dict[str, ndarray]

Call self as a function.

observation_model(params: ndarray) → ndarray

Runs a Lotka-Volterra simulation for T time steps and returns subsample evenly spaced points from the simulated trajectory, given contact parameters params.

Parameters:

paramsnp.ndarray of shape (2,)

The 2-dimensional vector of disease parameters.

Returns:

xnp.ndarray of shape (subsample, 2) or (subsample*2,) if subsample is not None,

otherwise shape (T, 2) or (T*2,) if subsample is None. The time series of simulated predator and pray populations

rejection_sample(batch_shape: tuple[int, ...], predicate: Callable[[dict[str, ndarray]], ndarray], *, axis: int = 0, sample_size: int = None, **kwargs) → dict[str, ndarray]

sample(batch_shape: tuple[int, ...], **kwargs) → dict[str, ndarray]

Runs simulated benchmark and returns batch_size parameter and observation batches

Parameters:

batch_shape: tuple

Number of parameter-observation batches to simulate.

Returns:

dict[str, np.ndarray]: simulated parameters and observables

with shapes (batch_size, …)