Hi Devs,

I am working on AR1 process with stochastic volatility.

The model is formulated as below:

sigma_t ~ sigma + epsilon_t

y_t ~ Normal(theta * y_{t-1}, sigma_t)

y_t is observable.

I want to estimate sigma and theta. The model has epsilon as a noise, epsilon_t ~ Expo(1).

The code is shown below:

```
def SV(y):
theta = numpyro.sample("theta", dist.Normal())
sigma = numpyro.sample("sigma",dist.Exponential())
def transition_func(carry, y):
prev_y = carry
diff_sigma = numpyro.sample("diff_sigma",dist.Exponential())
current_y = numpyro.sample("current_y", dist.Normal(theta * prev_y, (sigma + diff_sigma)), obs = y)
return (current_y), None
carry_init = (y[0])
scan(transition_func, carry_init, y[1:],history =len(y))
```

It seems that HMC also tries to estimate the noise, epsilon_t.

How can I make a model that does not consider the noise as a to-be-estimated parameters?

Thanks for any helps.