Based on prior knowledge, let's analyze the relationship between the feature "IBS_POST" and the task of determining if the patient has chronic heart failure.

Given that "IBS_POST" represents the coronary heart disease in recent weeks, days before admission to the hospital, it can provide some insights into the patient's condition. The possible categories for this feature are:
- "there was no СHD"
- "exertional angina pectoris"
- "unstable angina pectoris"

To determine if the myocardial infarction complications data shows chronic heart failure, we need to analyze the relationship between the "IBS_POST" feature and the target variable.

Since there is no specific mention of chronic heart failure in the feature description, we cannot directly establish a clear relationship between "IBS_POST" and chronic heart failure. However, based on our prior knowledge, we can infer that patients with "unstable angina pectoris" are more likely to experience complications, including chronic heart failure, compared to those with "exertional angina pectoris" or "there was no СHD". Therefore, we can assume that if a patient has "unstable angina pectoris" as their value of "IBS_POST", the likelihood of chronic heart failure may be higher.

Based on this analysis, we can generate the following dictionary:

```json
{
	"no": ["there was no СHD", "exertional angina pectoris"],  
	"yes": ["unstable angina pectoris"]
}
```

Please note that the values in the "no" class are "there was no СHD" and "exertional angina pectoris", which might indicate a lower likelihood of chronic heart failure. The value "unstable angina pectoris" is included in the "yes" class, indicating a higher likelihood of chronic heart failure based on prior knowledge.