Hi all,

I am new to Stan and am trying to use it to fit a linear model where the fraction of outliers are unknown. I have been able to write out a model in python to use with Emcee but I want to use it in Stan.

So far I have gotten my basic linear model (with no outliers) to work

```
line_model = """
data {{
int<lower=0> N;
real<lower=0> sigma;
real y[N];
real x[N];
}}
parameters {{
real m;
real c;
}}
transformed parameters {{
real theta[N];
for (j in 1:N)
theta[j] = m*x[j] + c;
}}
model {{
m ~ normal({mlower}, {mupper});
c ~ normal({clower}, {cupper});
y ~ normal(theta, sigma);
}}
"""
```

What I am stuck on is adding in some sort of parameter that represents the fraction of outliers in the data. I am trying to follow what has been done in this blog but adapting it for Stan.

If this is possible and anyone knows how to do it I would greatly appreciate some help, I have been stuck on this for a few days,

https://dfm.io/posts/mixture-models/

In my case I only care about parameters m, b and Q

Thanks,