Based on prior knowledge, the credit_history feature could potentially be a strong indicator of whether a person receives credit or not. Different categories of credit history could reflect the person's ability to manage their credit obligations and demonstrate their creditworthiness.

To analyze the relationship between the credit_history feature and the task of determining whether a person receives credit or not, we can examine the distribution of credit_history categories for each target class (yes and no).

Here is the analysis of the relationship between the credit_history feature and the target variable:

- For the "yes" class (person receives credit):

  - Possible values of credit_history: ['existing paid', 'no credits/all paid', 'all paid']
    - These categories suggest a positive credit history where the person has existing paid credits, no previous delayed credits, or has cleared all previous credits.
  
- For the "no" class (person does not receive credit):

  - Possible values of credit_history: ['critical/other existing credit', 'delayed previously']
    - These categories suggest a negative credit history where the person has critical or other existing credits that have not been paid, or has delayed previous credits.

Based on this analysis, here is the dictionary representing the relationship between the feature credit_history and the target variable:

```json
{
	"yes": ["existing paid", "no credits/all paid", "all paid"],
	"no": ["critical/other existing credit", "delayed previously"]
}
```

Please note that this dictionary represents the relationship between the feature and the target variable based on the provided information and analysis. There might be additional factors or features that could influence the final decision on whether a person receives credit or not.