The transition function in the case of a Simple SIR model is defined as:

```
def transition(self, params, state, t):
R0, tau, rho = params
# Sample flows between compartments.
S2I = pyro.sample("S2I_{}".format(t),
infection_dist(individual_rate=R0 / tau,
num_susceptible=state["S"],
num_infectious=state["I"],
population=self.population))
I2R = pyro.sample("I2R_{}".format(t),
binomial_dist(state["I"], 1 / tau))
# Update compartments with flows.
state["S"] = state["S"] - S2I
state["I"] = state["I"] + S2I - I2R
# Condition on observations.
t_is_observed = isinstance(t, slice) or t < self.duration
pyro.sample("obs_{}".format(t),
binomial_dist(S2I, rho),
obs=self.data[t] if t_is_observed else None)
```

Would I be correct in my hypothesis that `rho`

is used to represent the fact that the reported data is different from the true underlying distribution of the compartments due to limited testing?