Based on prior knowledge, the credit_history feature can provide insights into whether a person receives credit or not. Here is the analysis of the relationship between the feature and the target:

- "critical/other existing credit": This category suggests a critical or uncertain credit history, which may increase the likelihood of the person not receiving credit. Hence, it is likely to be present in the "no" class.
- "existing paid": This category indicates that the person has a previously existing credit that has been paid. It could be an indication of a favorable credit history, increasing the chances of receiving credit. Hence, it is likely to be present in the "yes" class.
- "delayed previously": This category implies that the person has experienced delayed payments in the past. Such credit history might decrease the chances of receiving credit, making it likely to be present in the "no" class.
- "no credits/all paid": This category implies that the person either doesn't have any previous credits or has paid off all previous credits. It could be seen as a positive credit history, increasing the likelihood of receiving credit. Hence, it is likely to be present in the "yes" class.
- "all paid": This category suggests that the person has paid off all previous credits. It indicates a positive credit history that might increase the chances of receiving credit and is likely to be present in the "yes" class.

Based on this analysis, the dictionary for the relationship between the credit_history feature and the credit receiving task would be:

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

Please note that the dictionary includes the plausible categories for each target class, as mentioned in the analysis.