Based on prior knowledge, we can analyze the relationship between the "existing_credits" feature and the task of determining whether a person receives a credit or not.

In general, we can make the following assumptions:

1. Higher values of "existing_credits" may indicate a higher chance of the person not receiving a credit. This is because having multiple existing credits may make a person appear overburdened with debt, potentially leading to a higher credit risk.

2. Lower values of "existing_credits" may indicate a higher chance of the person receiving a credit. This is because having fewer existing credits may suggest better financial stability and a lower credit risk.

Based on these assumptions, we can create a dictionary with specific values for the "existing_credits" feature for each target class:

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0],
	"no": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

In this dictionary, we have included 5 typical values for each target class. For the "yes" class, the values range from 0.0 to 4.0, representing lower numbers of existing credits. For the "no" class, the values range from 1.0 to 5.0, representing higher numbers of existing credits. These values are just examples and can vary depending on the specific dataset being analyzed.