bbbales2

import pystan
import numpy
import matplotlib.pyplot as plt

# We gotta only have a few datapoints. Too much data and the mistake isn't visible
y = numpy.random.lognormal(mean = 0.7, sigma = 1.0, size = 5)

# This model does not have the adjustment and so is incorrect
model_code = """
data {

bbbales2

import pystan
import numpy
import matplotlib.pyplot as plt

y = numpy.random.lognormal(mean = 0.7, sigma = 1.0, size = 5)

# Both of these models produce the same results
model_code = """
data {
  int<lower=1> N;

bbbales2

ITEM: TIMESTEP
1000
ITEM: NUMBER OF ATOMS
38
ITEM: BOX BOUNDS
0 2.801
0 1.001
0 1.001
ITEM: ATOMS
892916343 5 0.188849 0.248032 0.763223