Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_01" and the presence of chronic heart failure in myocardial infarction complications data.

To determine if there is a relationship, we can look at the distribution of "fibr_ter_01" values for each target class ("no" and "yes").

Let's assume we have a dataset with the following observations:

| fibr_ter_01 | Chronic Heart Failure |
|-------------|-----------------------|
| no          | no                    |
| yes         | yes                   |
| no          | no                    |
| no          | yes                   |
| yes         | no                    |
| ...         | ...                   |

By analyzing this dataset, we can create the following dictionary:

```json
{
    "no": ["no"],
    "yes": ["yes"]
}
```

In this case, the feature "fibr_ter_01" only has one distinct value within each target class. We can conclude that for the given dataset, the presence of chronic heart failure does not show a clear relationship with the feature "fibr_ter_01". The dictionary above represents the possible values of "fibr_ter_01" for each target class, where "no" corresponds to the absence of chronic heart failure, and "yes" corresponds to the presence of chronic heart failure.