To analyze the relationship between the feature "IBS_POST" and the presence of chronic heart failure, we need to examine the distribution of different categories of "IBS_POST" for the target variable.

Based on the feature description, the categories of "IBS_POST" are:
- there was no СHD
- exertional angina pectoris
- unstable angina pectoris

To create the dictionary, we need to identify the possible values for "IBS_POST" that correspond to the target class "no" (indicating no chronic heart failure) and the possible values that correspond to the target class "yes" (indicating chronic heart failure).

Without specific information about the relationship between the feature and target, we cannot make accurate predictions. However, we can assume that certain categories may be more likely to indicate chronic heart failure based on their nature and existing medical knowledge.

Based on prior knowledge, we can create the dictionary as follows:

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

Please note that the list for the target class "yes" includes categories that are more likely to indicate chronic heart failure, while the target class "no" includes only one category that explicitly states the absence of coronary heart disease.

This dictionary represents the possible values of "IBS_POST" for each target class, but it is essential to remember that without further information or statistical analysis, the relationship between the feature and the task cannot be definitively determined.