bayesflow.benchmarks.lotka_volterra module#

bayesflow.benchmarks.lotka_volterra.prior(rng=None)[source]#

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

Parameters:
rngnp.random.Generator or None, default: None

An optional random number generator to use.

Returns:
thetanp.ndarray of shape (4,)

A single draw from the 4-dimensional prior.

bayesflow.benchmarks.lotka_volterra.simulator(theta, X0=30, Y0=1, T=20, subsample=10, flatten=True, obs_noise=0.1, rng=None)[source]#

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

See https://arxiv.org/pdf/2101.04653.pdf, Benchmark Task T.10.

Parameters:
thetanp.ndarray of shape (2,)

The 2-dimensional vector of disease parameters.

X0float, optional, default: 30

Initial number of prey species.

Y0float, optional, default: 1

Initial number of predator species.

TT, optional, default: 20

The duration (time horizon) of the simulation.

subsampleint 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.

flattenbool, optional, default: True

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

obs_noisefloat, optional, default: 0.1

The standard deviation of the log-normal likelihood.

rngnp.random.Generator or None, default: None

An optional random number generator to use.

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

bayesflow.benchmarks.lotka_volterra.configurator(forward_dict, mode='posterior', scale_data=1000, as_summary_condition=False)[source]#

Configures simulator outputs for use in BayesFlow training.