Where I Can find Stan+R code for your paper “Comparison of Bayesian predictive methods for model selection”???

I really need them and could not find any more way to do my analysis project.

Can you help me?? ]]>

Where I Can find Stan+R code for your paper “Comparison of Bayesian predictive methods for model selection”???

I really need them and could not find any more way to do my analysis project.

Can you help me?? ]]>

> (selection). Or at least that’s my reading of it.

I think I’m using Bayesian theory following Bernardo & Smith (1994), who combine the Bayesian theory and decision theory. I’m trying to make the best decision (as described in our review paper), we just need to use approximation for the future data distribution and approximations for some integrals, but that doesn’t make it non-Bayesian. You could say it’s approximative-Bayesian, but then most of Bayesian inference is approximative. (There are people who say that using Monte Catlo is non-Bayesian, too)

Aki

]]>I think the difference between this and what Aki is suggesting, is that there isn’t really any selection in these methods. My favourite bit of Aki’s approach is that it uses a non-Bayesian solution to do a not naturally Bayesian thing (selection). Or at least that’s my reading of it.

Unrelated: I do like the thinking under the Yang and Dunson paper, where they get sparsity by explicitly constructing a prior with mass on sparse models, which strikes me as a more sensible way forward than, say, the horseshoe, where a bunch of independent priors with big tails magically give sparsity. Sparsity is a joint property, so it should be specified using dependent priors. (That being said, I can’t actually reproduce some of the calculations needed to make the proofs work, which is always nerve-wracking)

]]>This is really cool. have a thought, which is related to the idea I had earlier for comparing 2 models. Suppose you fit M different models to the data, and you have N data points. Then you have a N*M matrix of prediction errors. Or, actually, since you don’t observe the true parameter values, you have an N*M*S array of data*models*simulation draws, where each element in that matrix is a log posterior for data point n in model m based on posterior simulation draw s.’

The usual approach is to analyze each of the M models separately and, for each, come up with a summary such as LOO or WAIC. Instead perhaps it makes sense to fit a hierarchical model with data point effects and model effects and interactions.

I think this is related to your paper. The idea in your paper is probably better than the idea I have here, but maybe my idea could be useful in the context of what you are doing.

Also, I think the ideas in your paper are related to the recent work of Christian Robert et al. on using mixture models instead of model averaging.

My plan is to first work this out in the simple setting where just two models are being compared, so that multiple comparisons is not such a concern and we can focus on the comparison of estimated expected prediction error.

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