Based on prior knowledge, we need to analyze the relationship between the feature "fibr_ter_07" and the target variable indicating the presence of chronic heart failure in myocardial infarction complications.

To do this analysis, we'll first examine the possible values of the feature and observe their distribution for the target classes.

Here are the steps to analyze the relationship and generate the dictionary:

1. Identify all the unique values of the feature "fibr_ter_07": ['no', 'yes']

2. For each unique value, identify the distribution across the target classes:

    - For the "no" target class, identify the possible values of "fibr_ter_07": ['no', 'yes']

    - For the "yes" target class, identify the possible values of "fibr_ter_07": ['no', 'yes']

3. Create the dictionary using the identified values:

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

Note: In this case, the values of "fibr_ter_07" cover all the possible values of the feature for both target classes. Therefore, the lists for each target class include all the possible values of "fibr_ter_07".