Bayesian goodness of fit

I was impressed by this paper introduced in lunch meeting.

It is a difficult problem to assess the goodness of fit, especially for the model with large number of parameters. In contrast to the frequentist’s point of view, this problem is considered as an inference problem in this paper.

Anyway, the gist of this paper is,

1. Suppose that the posterior is REALLY the correct distribution generating data points by random sampling. This is a null hypothesis (H0).

2. Slightly perturb the posterior using a function. This is an alternative hypothesis (H1).

3. Then calculate Bayes factor, B10. If the null hypothesis is supported, the model is good. Otherwise, the model is bad. It’s simple !

Computation is done by post processing the posterior. All the gory details are matters of implementation which doesn’t look very straightforward to me though.


~ by ilsangyoon on July 22, 2010.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: