Respondents replied in the following way:
Yes: they will be attending
No: they won't be attending
Maybe: they attach a percentage certainty as an estimate that they'll be attending. E.g. 40% sure I'll make it or 65% sure I'll make it.
I've recently read some literature on Bayesian modeling. Far from knowledgeable in this field but keen to use it for this example and learn in the process.
I intend for the model to be pessimistic so it can account for the worst as the goal of the model is to give an idea of how many people will be attending.
- The count data observed is modeled by a binomial distribution. I choose it because it is discrete and accounts for there only being two observable states : attends and doesn't attend. So that leaves us with parameters N and p.
- Modeling p is where it gets difficult. We model p in 3 ways.
- To conserve the pessimism, we assume that even though people say they are coming there is still between 50% to 100% chance that they will attend. Therefore $p_{1} \sim$ Uniform($0.5,1$).
- The idea behind the percentage given for maybe-repsonse is to use the trial roulette method of eliciting expert priors. So we end up with some data to which we can fit a normal distribution to. Hence $p_{2} \sim$ Norm($\mu,\sigma$).
- Cool. I think I'm making sense....perhaps. Now we assume the worst of people such that if a response of no, then they definitely will not attend with 100% probability. Should I model this just as a deterministic value: $p_{3} = 0$
How would I go about putting all this together? I'm using pymc to try and model this. Should I create my own distribution function for $p$? E.g.
$$ p(c) = \begin{cases} U(0.5,1) & \quad \text{if } c_{i} = 1 \\\mathcal{N}(\mu,\sigma) & \quad \text{if } 0<c_{i}<1 \\ 0 & \quad \text{if }c_{i} = 0 \end{cases} $$ Where $c_{i}$ is the set of responses and $c_{i} = 1$ being a yes, 0 a No and and in between value a Maybe.
Would you tell me if what I am doing is absolute nonsense and if I'm on the right path would you please guide me in the right direction. Thank you for your time.