Based on prior knowledge, the relationship between the credit_amount feature and the task of whether a person receives credit (yes or no) can be analyzed as follows:

The credit_amount feature represents the numeric value of the credit amount. It is likely that the credit amount would play a role in determining whether a person receives credit or not. Generally, higher credit amounts might indicate higher risk or more significant financial obligations, which may influence the decision to provide credit.

To create the requested dictionary, let's assume these are typical ranges for the credit_amount based on our prior knowledge:

- Small credit amounts: 500 to 5,000
- Medium credit amounts: 5,000 to 15,000
- High credit amounts: 15,000 to 30,000
- Very high credit amounts: 30,000 to 50,000
- Extremely high credit amounts: 50,000 to 100,000

Based on these ranges, we can create the following dictionary:

```json
{
    "yes": [500.0, 1200.0, 7000.0, 10000.0, 25000.0],
    "no": [10000.0, 3000.0, 15000.0, 8000.0, 500.0]
}
```

In this example, the credit_amount values for the "yes" class range from 500.0 to 25,000.0, and for the "no" class, they range from 500.0 to 15,000.0.

Please note that these ranges are hypothetical, and in a real analysis, you would need to gather data and perform statistical analysis to determine the actual ranges and typical credit amount values for each target class.