Passing Two List to a Python function

Question

I am trying to run python package pyabc(Approximate Bayesian Computation) for model selection between two list of values i.e model_1=[2,3,4,5] and model_2=[3,4,2,5]. The main function of pyabc is ABCSMC which states that

Definition : ABCSMC(models: Union[List[Model], Model], parameter_priors:
Union[List[Distribution], Distribution, Callable], distance_function: Union[Distance,
Callable]=None, population_size: Union[PopulationStrategy, int]=100, summary_statistics:
Callable[[model_output], dict]=identity, model_prior: RV=None)

I don't know where to define and pass my two lists model_1 and model_2 in the below mentioned code. I tried it several times but not able to do it as I am new to Python. I am following an example and its code in mentioned below.

import os
import tempfile
import scipy.stats as st  
import pyabc

# Define a gaussian model 
sigma = .5

def model(parameters):
    # sample from a gaussian
    y = st.norm(parameters.x, sigma).rvs()
    # return the sample as dictionary
    return {"y": y}

# We define two models, but they are identical so far
models = [model, model]


# However, our models' priors are not the same.
# Their mean differs.
mu_x_1, mu_x_2 = 0, 1
parameter_priors = [
    pyabc.Distribution(x=pyabc.RV("norm", mu_x_1, sigma)),
    pyabc.Distribution(x=pyabc.RV("norm", mu_x_2, sigma))
]
abc = pyabc.ABCSMC(
    models, parameter_priors,
    pyabc.PercentileDistance(measures_to_use=["y"]))

db_path = ("sqlite:///" +
           os.path.join(tempfile.gettempdir(), "test.db"))
history = abc.new(db_path, {"y": y_observed})
print("ABC-SMC run ID:", history.id)
# We run the ABC until either criterion is met
history = abc.run(minimum_epsilon=0.2, max_nr_populations=5)

Answer 1

Model selection in pyABC aims to decide among different model candidates which model describes a common set of observed data best. In your above code, you would thus typically use different models in the list of models models = [model1, model2]. I am not sure what you mean by model selection over lists?

For the underlying algorithm and problem that it tries to solve, see also the original paper https://doi.org/10.1093/bioinformatics/btp619.