Based on prior knowledge, we can analyze the relationship between the feature "pdays" and the task of whether the client subscribes to a term deposit.

Since "pdays" represents the number of days that passed after the client was last contacted from a previous campaign, we can make the following assumptions:

1. If "pdays" is a very low value (e.g., less than 5), it could indicate that the client was recently contacted, which might result in a higher possibility of them subscribing to a term deposit.
2. If "pdays" is a very high value (e.g., greater than 100), it could indicate that a significant amount of time has passed since the last contact, which might result in a lower possibility of the client subscribing to a term deposit.

Based on these assumptions, we can create a dictionary with typical "pdays" values for the target classes 'no' and 'yes':

```json
{
	"no": [150.0, 200.0, 300.0, 400.0, 600.0],
	"yes": [2.0, 3.0, 4.0, 5.0, 8.0]
}
```

In this dictionary, the "no" class includes typical "pdays" values of 150.0, 200.0, 300.0, 400.0, and 600.0, while the "yes" class includes typical "pdays" values of 2.0, 3.0, 4.0, 5.0, and 8.0.

Please note that these values are illustrative and based on assumptions. A more thorough analysis using actual data would provide more accurate and specific information.