In order to analyze the relationship between the "credit_history" feature and the target variable of whether a person receives a credit or not, we need to examine the distribution of "credit_history" values for each class.

By examining the relevance of "credit_history" to whether a person receives a credit or not, we can identify the possible values for each target class.

Here's the dictionary representing the relationship between the "credit_history" feature and the target variable:

```json
{
    "yes": ["existing paid", "no credits/all paid", "all paid"],
    "no": ["critical/other existing credit", "delayed previously"]
}
```

From this analysis, we can see that the "yes" class, indicating that a person receives credit, is associated with the values "existing paid", "no credits/all paid", and "all paid" for the "credit_history" feature.

On the other hand, the "no" class, indicating that a person does not receive credit, is associated with the values "critical/other existing credit" and "delayed previously" for the "credit_history" feature.

Please note that this analysis is based on the given feature description and may not capture all possible relationships. However, it provides an initial understanding of how the "credit_history" feature relates to the target variable.