# Determining the Best Model in JASP

Hello JASP users, this is my first time using JASP and I have very little knowledge about statistics. I will determine the best ensemble of 12 climate models in simulating rainfall. I have been running with the Bayesian Linear Regression in JASP, but I am still confuse in determining the best model criteria. Is it based on BFm, B10 or R2? Furthermore, if we have decide one best model, which bayesian linier regression coefficient do we use to predict rainfall? Does it use the mean coefficient, SD coefficient, P (incl / data), or BF inclusion (all in the Posterior Summary table)?

Thank you so much for your help.

## Comments

This is the output

Hi Uhandoko,

We are finishing up a tutorial paper on this. I have asked the first author to send you a draft. Quickly though:

Cheers,

E.J.

Thank you very much EJ for your quick response and your kindness will send me a draft about this tutorial.

Sorry, I still don't understand the second answer. As I understand from your answer to predict something with Bayesian regression equation, the coefficients regression for each variable using "mean" column in the posterior summary table. For example, from the output I get, the first ranking model consisted of 12 climate models. So to predict rain, I will use the equation as follows:

Rainfall prediction = 8.687 + (- 0.450 * NorESM) + (- 0.299 * ACCESS) + (- 0.082 * bcc) + (- 0.085 * BNU) + (0.905 * CanESm2) + ... + (0.02 * MRI).

Is it like that?

Best regard..

Uhandoko

Dear Uhandoko,

There is a whole literature on how to predict exactly. In general, I would say you want the uncertainty surrounding your point prediction, so you'd want to take the distributions for the beta's into account (rather than just focusing on the means). Basically, I would do the following:

This way you take into account uncertainty in the beta's and in the models. But whatever you do, both need to be taken into account or else the prediction will be overconfident.

Cheers,

E.J.

Thank you for the advice for me, EJ

Best regard...

Uhandoko