Based on prior knowledge, we can analyze the relationship between the "credit_history" feature and the target variable of whether the person receives a credit or not.

Looking at the possible categories of the "credit_history" feature, we can make some assumptions about how they might relate to the target variable:

1. A "critical" credit history may indicate a higher risk for potential lenders, which could lead to the person not receiving a credit.

2. An "existing paid" credit history may indicate a lower risk for potential lenders, which could increase the likelihood of the person receiving a credit.

3. A "delayed previously" credit history may indicate some risk for potential lenders, but it is not as severe as a "critical" credit history. The impact on receiving a credit may depend on other factors.

4. A "no credits/all paid" credit history may suggest that the person has never had any credit or has always paid their credits on time. This could increase the likelihood of the person receiving a credit.

5. An "all paid" credit history suggests that all previous credits have been paid, indicating lower risk and potentially increasing the likelihood of the person receiving a credit.

Based on these assumptions, we can create the dictionary:

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

This dictionary specifies the possible values of the "credit_history" feature for each target class. Note that for the target class "yes", we included the values that are more likely to result in a person receiving a credit. For the target class "no", we included the values that are more likely to result in a person not receiving a credit.