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

It is commonly observed that younger individuals may have more difficulty obtaining credit due to limited credit history and possibly lower income. On the other hand, older individuals may also face challenges due to retirement or fixed income. Additionally, young adults may not yet have established a solid credit history, while older individuals may have more financial responsibilities.

Considering these factors, we can assume that there might be an inverse relationship between age and receiving credit, with a peak age range where credit approval is more likely. However, the precise age ranges cannot be determined without further context or data.

Based on this analysis, we can generate a dictionary with typical age values for each target class:

```json
{
    "yes": [25.5, 30.2, 35.9, 40.1, 45.7],
    "no": [18.3, 20.8, 22.6, 19.9, 23.5]
}
```
Please note that the age values provided here are for illustrative purposes only and do not reflect specific data. The actual age ranges and values may vary depending on the dataset being analyzed.