Based on prior knowledge, the marital status can potentially be a useful indicator in predicting whether a client will subscribe to a term deposit or not. Different marital statuses may be associated with different levels of financial stability or commitments, which could affect the decision to subscribe to a term deposit.

To perform the analysis, we can start by grouping the data based on the target variable (subscription to a term deposit). Then, we can examine the distribution of marital status within each group.

Here's the analysis:

- For clients who did not subscribe to a term deposit (target class "no"), the possible values of marital status are:
  - ['single', 'divorced', 'married']

- For clients who subscribed to a term deposit (target class "yes"), the possible values of marital status are:
  - ['single', 'married', 'divorced']

Note: The order of marital status values may vary based on the distribution of the data, but the possible values remain the same.

Based on this analysis, we can create the dictionary:

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

This dictionary indicates the possible values of the "marital" feature for each target class.