def get_model(n_obs=50, true_params=None, seed_obs=None, stochastic=True):
"""Return a complete Ricker model in inference task.
This is a simplified example that achieves reasonable predictions. For more extensive treatment
and description using 13 summary statistics, see:
Wood, S. N. (2010) Statistical inference for noisy nonlinear ecological dynamic systems,
Nature 466, 1102–1107.
Parameters
----------
n_obs : int, optional
Number of observations.
true_params : list, optional
Parameters with which the observed data is generated.
seed_obs : int, optional
Seed for the observed data generation.
stochastic : bool, optional
Whether to use the stochastic or deterministic Ricker model.
Returns
-------
m : elfi.ElfiModel
"""
if stochastic:
simulator = partial(stochastic_ricker, n_obs=n_obs)
if true_params is None:
true_params = [3.8, 0.3, 10.]
else:
simulator = partial(ricker, n_obs=n_obs)
if true_params is None:
true_params = [3.8]
m = elfi.ElfiModel()
y_obs = simulator(*true_params, n_obs=n_obs, random_state=np.random.RandomState(seed_obs))
sim_fn = partial(simulator, n_obs=n_obs)
sumstats = []
if stochastic:
elfi.Prior(ss.expon, np.e, 2, model=m, name='t1')
elfi.Prior(ss.truncnorm, 0, 5, model=m, name='t2')
elfi.Prior(ss.uniform, 0, 100, model=m, name='t3')
elfi.Simulator(sim_fn, m['t1'], m['t2'], m['t3'], observed=y_obs, name='Ricker')
sumstats.append(elfi.Summary(partial(np.mean, axis=1), m['Ricker'], name='Mean'))
sumstats.append(elfi.Summary(partial(np.var, axis=1), m['Ricker'], name='Var'))
sumstats.append(elfi.Summary(num_zeros, m['Ricker'], name='#0'))
elfi.Discrepancy(chi_squared, *sumstats, name='d')
else: # very simple deterministic case
elfi.Prior(ss.expon, np.e, model=m, name='t1')
elfi.Simulator(sim_fn, m['t1'], observed=y_obs, name='Ricker')
sumstats.append(elfi.Summary(partial(np.mean, axis=1), m['Ricker'], name='Mean'))
elfi.Distance('euclidean', *sumstats, name='d')
return m
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