Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_08" and the presence of chronic heart failure in patients with myocardial infarction complications.

To do this, we can compare the distribution of "fibr_ter_08" for patients with and without chronic heart failure. We can then determine the possible values of "fibr_ter_08" for each target class (yes or no).

Let's assume we have a dataset containing patients' fibr_ter_08 values and whether they have chronic heart failure. We can perform the analysis by filtering the dataset based on the target class and observing the distinct values of "fibr_ter_08" for each class.

Based on the dataset, the possible values of "fibr_ter_08" for the target class 'no' (patients without chronic heart failure) might include: ['yes', 'no'].

The possible values of "fibr_ter_08" for the target class 'yes' (patients with chronic heart failure) might include: ['yes', 'no'] as well.

Therefore, the dictionary representing the analysis would be:

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

Note that in this case, the possible values for "fibr_ter_08" are the same for both target classes.