def METHOD_NAME(cls):
    """ Prepare a problem for testing. """
    # Create toy model
    cls.model = toy.LogisticModel()
    cls.real_parameters = [0.015, 500]
    cls.times = np.linspace(0, 1000, 1000)
    cls.values = cls.model.simulate(cls.real_parameters, cls.times)
    # Add noise
    cls.noise = 10
    cls.values += np.random.normal(0, cls.noise, cls.values.shape)
    cls.real_parameters.append(cls.noise)
    cls.real_parameters = np.array(cls.real_parameters)
    # Create an object with links to the model and time series
    cls.problem = pints.SingleOutputProblem(
        cls.model, cls.times, cls.values)
    # Create a uniform prior over both the parameters and the new noise
    # variable
    cls.log_prior = pints.UniformLogPrior(
        [0.01, 400, cls.noise * 0.1],
        [0.02, 600, cls.noise * 100]
    )
    # Create a log likelihood
    cls.log_likelihood = pints.GaussianLogLikelihood(cls.problem)
    # Create an un-normalised log-posterior (log-likelihood + log-prior)
    cls.log_posterior = pints.LogPosterior(
        cls.log_likelihood, cls.log_prior)