Based on prior knowledge, we can analyze the relationship between the feature "Frequency" (total number of donations) and the target variable "Did the person donate blood?". 

In general, we can expect that individuals who have donated blood more frequently would be more likely to donate again in the future, leading to a higher probability of the target variable being "yes". Conversely, individuals who have donated blood less frequently may be less likely to donate again, resulting in a higher probability of the target variable being "no".

To create the dictionary with specific details, we need to analyze the possible values of the feature "Frequency" for each target class.

Let's assume the following frequency ranges:
- Individuals who have donated blood more than 10 times are considered to have high frequency donations.
- Individuals who have donated blood between 6 and 10 times are considered to have medium frequency donations.
- Individuals who have donated blood between 1 and 5 times are considered to have low frequency donations.
- Individuals who have never donated blood are considered to have zero frequency donations.

Based on these assumptions, we can generate the dictionary:

```json
{
	"yes": [11.0, 12.0, 15.0, 20.0, 25.0],
	"no": [0.0, 2.0, 4.0, 6.0, 8.0]
}
```

Please note that these are just example values and ranges based on assumptions. The actual ranges and values may differ based on the specific dataset and context.