# Bayesian Approach To Combine Multiple Weighted Inputs

#### cmarin

##### New Member
I'm beginning to learn about Bayesian theory but I'm stumped on the ideal approach for combining multiple weighted inputs. Here's an example to make this more concrete. Let's say that I want to determine the probability that John will like a particular cookie. I know that generally John likes 10% of the cookies he tries. Jill tries the cookie beforehand and says that there is a 70% chance that John will like the cookie. Jill and John been to many cookie tasting events beforehand, so I want to weigh her input highly (say 0.8). Jack also tries the cookie and thinks there is a 60% chance John will like the cookie. However he and John have not been to many cookie tasting events together so he's not as reliable an input as Jill, so I'd want to weigh his input less, say at 0.2.

Given what I know, how do I calculate the overall probability that John will like that particular cookie using a Bayesian approach? Once I figure out the mechanism I'll eventually want to apply this to a real world problem using either R or Python and there will also be more than two weighted inputs.

#### staassis

##### Active Member
This question is not as much about Bayesian statistics as about the joint distribution of the preferences of John, Jill and Jack. You cannot answer this question without postulating correlation between John, Jill and Jack. After you've done that, you simply solve for the conditional distribution of John given Jill and Jack. Statements like "say 0.8" or "say 0.2" are non-scientific.

#### cmarin

##### New Member
This question is not as much about Bayesian statistics as about the joint distribution of the preferences of John, Jill and Jack. You cannot answer this question without postulating correlation between John, Jill and Jack. After you've done that, you simply solve for the conditional distribution of John given Jill and Jack. Statements like "say 0.8" or "say 0.2" are non-scientific.