def METHOD_NAME(vec: List[Any], trials: int = 2000) -> float:
    """Returns the empirical likelihood (via Monte Carlo trials) of randomly finding a vector with as low entropy as this one."""
    n = np.nansum(vec)
    newvec = list(filter(lambda x: not np.isnan(x), vec))
    m = len(newvec)
    ne = normalized_entropy(newvec)
    sampled_vec = rng.multinomial(n, [1 / m for i in range(m)], trials)
    # Return the fraction of times the sampled vector has no more entropy than the original vector
    return sum(normalized_entropy(v) <= ne for v in sampled_vec) / trials