Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_05" and the presence of chronic heart failure in the myocardial infarction complications data.

To determine if there is a relationship, we can analyze the distribution of "fibr_ter_05" values for patients with and without chronic heart failure.

Once we have analyzed the data, we can create a dictionary that includes the possible values of "fibr_ter_05" for each target class (yes and no).

Here is the analysis and the resulting dictionary:

Analysis:
- Patients with chronic heart failure (target class: yes) may have received fibrinolytic therapy (fibr_ter_05: yes) or not (fibr_ter_05: no). Therefore, both values are possible for this target class.
- Patients without chronic heart failure (target class: no) may or may not have received fibrinolytic therapy. Therefore, both values (fibr_ter_05: yes and fibr_ter_05: no) are also possible for this target class.

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

Note: The analysis assumes that the feature "fibr_ter_05" is relevant to the task and has a potential relationship with chronic heart failure. If there is insufficient data or conflicting information, the analysis may not accurately represent the relationship.