hi everyone - I’m thinking I may have miss-specified a Turing model

I want to combine measurements of X, from 2 tests:

test A, has a known precision (`test_A_error`

)

test B has an unknown precision (`test_B_error`

) and bias (`test_B_bias_pr`

)

I’m expecting that increasing the amount of data from test A and from test B should reduce uncertainty in all my parameters: `mean_X`

, `sd_X`

, `X`

, `test_B_bias`

and `test_B_error`

…however, this is not the case. Specifically, `mean_X`

,` sd_X`

, `X`

, do not appear to be shifting from their (weakly informative) prior.

I will continue to investigate, but would like to knof if anyone sees anything fundamentally wrong with the model:

```
@model function test_model(test_A_data, test_A_error, test_B_data)
# priors
mean_X ~ mean_X_pr
sd_X ~ sd_X_pr
test_B_bias ~ test_B_bias_pr
test_B_error ~ test_B_error_pr
# Likelihood for the test data
X ~ Normal(mean_X, sd_X)
for test_A_id in eachindex(test_A_data)
test_A_data[test_id] ~ Normal(X, test_A_error)
end
for test_B_id in eachindex(test_B_data)
test_B_data[test_B_id] ~ Normal(X + test_B_bias, test_B_error)
end
end
```