lockdown_end incorrectly samples from a uniform distribution over [lockdown_start_min, lockdown_start_max]

[djinnome]

Hi folks,

lockdown_start_min = torch.tensor(0.5)
lockdown_start_max = torch.tensor(1.5)

lockdown_end_min = torch.tensor(2.0)
lockdown_end_max = torch.tensor(2.5)

def uncertain_intervened_sir(lockdown_strength, init_state, start_time, logging_times) -> State:
    lockdown_start = pyro.sample("lockdown_start", dist.Uniform(lockdown_start_min, lockdown_start_max))
    # lockdown_end samples from the same distribution as lockdown_start
    lockdown_end = pyro.sample("lockdown_end", dist.Uniform(lockdown_start_min, lockdown_start_max))
    return intervened_sir(lockdown_start, lockdown_end, lockdown_strength, init_state, start_time, logging_times)

should be:

lockdown_start_min = torch.tensor(0.5)
lockdown_start_max = torch.tensor(1.5)

lockdown_end_min = torch.tensor(2.0)
lockdown_end_max = torch.tensor(2.5)

def uncertain_intervened_sir(lockdown_strength, init_state, start_time, logging_times) -> State:
    lockdown_start = pyro.sample("lockdown_start", dist.Uniform(lockdown_start_min, lockdown_start_max))
    # lockdown_end samples from a different distribution than lockdown_start
    lockdown_end = pyro.sample("lockdown_end", dist.Uniform(lockdown_end_min, lockdown_end_max))
    return intervened_sir(lockdown_start, lockdown_end, lockdown_strength, init_state, start_time, logging_times)