Hi @cluhmann, sure. I am integrating some environmental data from various sources, using year as an index. Some of the data has been observed at yearly intervals, whereas other data has been observed bi-yearly, or in some cases every 5 years. So for example, I have data about CO2 emissions in the United States for every year between 1949-2020, and also data about air pollution in the United States for every other year within that interval. The data is missing on regular intervals, not at random.
I am able to successfully model the fully observed variables using a bi-modal NormalMixture. The problem is that the NormalMixture distribution is not compatible with observed data that has missing values.
In this post Ricardo suggests using a Potential to address this. However, the implementation example he provided didn’t work for me, and I didn’t really understand the solution well enough to troubleshoot it.