Based on prior knowledge, the marital status could be a potential predictor for whether a client subscribes to a term deposit or not. Let's analyze the relationship between the marital status feature and the target variable.

To do this, we need to examine the distribution of marital status for clients who subscribe to a term deposit (yes) and those who don't (no).

Here is a possible analysis:

- For the "no" class (clients who do not subscribe to a term deposit):
    - Possible values of marital status: ['married', 'single', 'divorced']
    - It is likely that clients who are single or divorced are less likely to subscribe to a term deposit compared to those who are married.
    - Therefore, we can include all possible values in the dictionary: ['married', 'single', 'divorced']

- For the "yes" class (clients who subscribe to a term deposit):
    - Possible values of marital status: ['married', 'single', 'divorced']
    - It is possible that married clients may be more likely to subscribe to a term deposit compared to single or divorced clients, as they may have more stable financial positions.
    - Therefore, we can include all possible values in the dictionary: ['married', 'single', 'divorced']

Based on the analysis, the dictionary we can create is:

```json
{
	"no": [
		"married", 
		"single", 
		"divorced"
	],
	"yes": [
		"married", 
		"single", 
		"divorced"
	]
}
```

Please note that the final decision on which values to include may depend on the specific dataset and domain knowledge.