Hi everyone,

I would like to sample from a known distribution in my model, and be sure that distribution is not accounted for when running the MCMC. At this time, when calling `numpyro.sample('Noise', dist.Normal(0, 1))`

and adding it to the observed value, the Noise parameter will be constrained like the others. I would like to know if there is a clean way to make sure this parameter stay unchanged with the MCMC

Iâ€™m unclear what you mean. You have some sort of additional noise as a part of your observations, and you want to remove those outliers? Could you express this graphically or in equation form?

Sorry if I am not clear enough, I really lack the words to express my issue in a Bayesian framework, Iâ€™ll do my best, letâ€™s steal youâ€™re introduction tutorial (neat job btw):

Just take the case of Y ~ m*X + c

```
def model(X,Y,E):
m = numpyro.sample('m', numpyro.distributions.Uniform(-5,5)) # prior on m
c = numpyro.sample('c', numpyro.distributions.Uniform(-5,5)) # Prior on c
y_model = m*X + c
for i in range(len(X)):
numpyro.sample('y_%i' %i, numpyro.distributions.Normal(y_model[i], E[i]), obs=Y[i])
```

My specific situation would be that I know the distribution of c, letâ€™s say c ~ N(0, 3.5). But if I declare `c = numpyro.sample('c', numpyro.distributions.Normal(0, 3.5))`

in the model, it will be treated as a prior distribution, and fitted as the sampling is progressing. I would like it to avoid this, so the distribution of c after sampling is such that c ~ N(0, 3.5). Is it clearer ?

Ah okay, I think I understand. You want your `samples`

for c to obey the prior distribution instead of the posterior distribution. Thereâ€™s two ways I can think of.

Firstly, just tweak your results such that the parameter is re-drawn from the prior, e.g.:

**Manually Overwrite Results**

```
samps = sampler.get_samples()
N = len(samps['c'])
samps['c'] = np.random.randn(N) * 3.5
```

The other approach is to use the effect handlers, a suite of tools for editing the behavior of models from the outside. I think the handler you want is â€śdoâ€ť, but having not tested it you might want â€śliftâ€ť. Handlers are called like so:

**Using effect handlers**

```
from numpyro import handlers
from numpyro import distributions as dist
altered_model = handlers.do(model, data = {"c", dist.Normal(0,3.5)})
samps = sampler.get_samples()
N = len(samps['c'])
samps['c'] = np.random.randn(N) * 3.5
```

Apologies if this example doesnâ€™t work perfectly, I didnâ€™t get a chance to test it myself. Let me know how it goes.

Hi Hugh,

Thank again for your feedbacks, for instance, if I simply over-write the posterior distributions, I would not be representative of what the sampler has explored during its run. I didnâ€™t know of effect handlers and how to use them, thank you for pointing this to me! It could have work if the site values could be fixed stochastically with a distribution (the `do`

handler set the prior distribution for a parameter from what I understood, and the `condition`

set a site to a given value).

If found a way around in this thread where the definition of a custom sampling for each site is achievable using `HMCGibbs`

. And this seems to work!