add model centred to autofit result

Author: Jammy2211

autofit/non_linear/result.py

@@ -123,6 +123,29 @@ def instance(self):
     def max_log_likelihood_instance(self):
         return self.instance
 
+    @property
+    def model_centred(self) -> AbstractPriorModel:
+        """
+        Returns a model where every free parameter is a `GaussianPrior` with `mean` the previous result's
+        inferred maximum log likelihood parameter values and `sigma` the input absolute value `a`.
+
+        For example, a previous result may infer a parameter to have a maximum log likelihood value of 2.
+
+        If this result is used for search chaining, `model_centred_absolute(a=0.1)` will assign this free parameter
+        `GaussianPrior(mean=2.0, sigma=0.1)` in the new model, where `sigma` is linked to the input `a`.
+
+        Parameters
+        ----------
+        a
+            The absolute width of gaussian priors
+
+        Returns
+        -------
+        A model mapper created by taking results from this search and creating priors with the defined absolute
+        width.
+        """
+        return self.samples_summary.model_centred
+
     def model_centred_absolute(self, a: float) -> AbstractPriorModel:
         """
         Returns a model where every free parameter is a `GaussianPrior` with `mean` the previous result's