Based on prior knowledge, we will analyze the relationship between the feature "fibr_ter_06" and the task of determining whether the patient has chronic heart failure.

To analyze this relationship, we can first gather data on patients who have myocardial infarction complications and categorize them based on whether they have chronic heart failure. We can then examine the distribution of the "fibr_ter_06" values within each group (patients with chronic heart failure vs. patients without chronic heart failure) to see if there are any patterns or significant differences.

Once we have analyzed the data, we can generate the requested dictionary with the possible values of "fibr_ter_06" for each target class.

Based on the analysis, let's assume the following observations:

Target class "no" (Patients without chronic heart failure):
- "fibr_ter_06" values: ['no', 'no', 'yes', 'no', 'yes', 'no']

Target class "yes" (Patients with chronic heart failure):
- "fibr_ter_06" values: ['no', 'yes', 'yes', 'yes', 'yes']

The generated dictionary would be:

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

Please note that this is a hypothetical example and the actual analysis would require real data for accurate results.