# Dawid-Skene-like model implementation: chain contains only divergences, model misspecified **URL:** \ **Category:** Questions\ **Created:** [August 27, 2020, 6:00pm UTC](https://discourse.pymc.io/t/dawid-skene-like-model-implementation-chain-contains-only-divergences-model-misspecified/5701 "2020-08-27T18:00:38Z")\ **Posts on this page:** 1\ **Showing post:** 4

**Author:** ![bramarco](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/bramarco/32/2767_2.png) [@bramarco](https://discourse.pymc.io/u/bramarco)\ **Post date:** [September 7, 2020, 3:48pm UTC](https://discourse.pymc.io/t/dawid-skene-like-model-implementation-chain-contains-only-divergences-model-misspecified/5701/4 "2020-09-07T15:48:30Z")

Hi everyone again, I am writing because I am trying to figure out how to handle point 3 of my previous reply to @AlexAndorra suggestion: I would like to write down a proper Likelihood taking into account that my data is truncated (to be honest, I think that “truncation” is not the correct word here because all the obs \< 0 are set zero). I am modelling the observations with a Normal Likelihood, but, as Alex made me notice, my observations can’t be negative. I’ve checked the solution for [censored data](https://docs.pymc.io/notebooks/censored_data.html) and for [truncated data](https://discourse.pymc.io/t/truncated-inverse-normal-distribution-also-known-as-wald-distribution/1125) but I think that my situation is different, given that all my negative observations are not truncated, but they are set to zero so that the histogram of my observation shows a high obs=0 bin. How can I make the model guess which obs=0 is really 0 and which one is more likely to be negative, imputed to 0? Obviously I’m referring to the model that is in my first post here. Thank you! --- _[View the full topic](https://discourse.pymc.io/t/dawid-skene-like-model-implementation-chain-contains-only-divergences-model-misspecified/5701)._