Based on prior knowledge, the credit_history feature can be analyzed as follows:

1. critical/other existing credit: This category suggests that the person has existing credit that is classified as critical or falls under some other category. It is reasonable to speculate that individuals with critical/other existing credit may have a higher chance of not receiving credit. Therefore, this category may likely be associated with the target class "no".

2. existing paid: This category implies that the person has an existing credit history and has paid off their previous credits. Individuals who have paid off their existing credits may have a higher chance of receiving credit again. Hence, this category may be associated with the target class "yes".

3. delayed previously: This category indicates that the person has experienced a delay in paying previous credits. Individuals who have a history of delayed payments may have a lower chance of receiving credit. Thus, this category may likely be associated with the target class "no".

4. no credits/all paid: This category suggests that the person does not have any previous credits or has paid off all their previous credits. Individuals with no credits or who have paid off all their previous credit may have a higher chance of receiving credit. Therefore, this category may be associated with the target class "yes".

5. all paid: This category implies that the person has paid off all their previous credits. Individuals who have paid off all their previous credits may have a higher chance of receiving credit again. Thus, this category may be associated with the target class "yes".

Based on this analysis, the dictionary can be constructed as follows:

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