Based on prior knowledge, we can analyze the relationship between the feature "persons" and the target variable "decision to buy this car".

To do this, we can start by grouping the data based on the target variable and looking at the distribution of the different categories of the "persons" feature within each target class.

Let's assume we have the following sample data:

| persons  | decision to buy this car |
|----------|--------------------------|
| 2        | good                     |
| 4        | very good                |
| 2        | acceptable               |
| more     | acceptable               |
| 2        | unacceptable             |
| 4        | good                     |
| more     | very good                |

Based on the above data, we can then generate the following dictionary:

```json
{
	"unacceptable": ["2"],
	"acceptable": ["2", "more"],
	"very good": ["4", "more"],
	"good": ["2", "4"]
}
```

Explanation of the dictionary:
- For the target class "unacceptable", the possible values of the "persons" feature are ["2"].
- For the target class "acceptable", the possible values of the "persons" feature are ["2", "more"].
- For the target class "very good", the possible values of the "persons" feature are ["4", "more"].
- For the target class "good", the possible values of the "persons" feature are ["2", "4"].

Note that we include any possible value of the "persons" feature that appears in each target class, even if it may be hard to predict. However, if there are no instances of a particular value of the "persons" feature within a target class, we would exclude it from the respective list in the dictionary to ensure that no list is empty.