# Model Comparison Bayesian Estimation (again)

**URL:** <https://forum.dynare.org/t/model-comparison-bayesian-estimation-again/3804>\
**Category:** ML/Bayesian estimation\
**Created:** [2 April 2014 11:39 UTC](https://forum.dynare.org/t/model-comparison-bayesian-estimation-again/3804 "2014-04-02T11:39:50Z")\
**Posts on this page:** 1\
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**Author:** ![lah89](https://forum.dynare.org/letter_avatar_proxy/v4/letter/l/8491ac/32.png) [@lah89](https://forum.dynare.org/u/lah89)\
**Post date:** [2 April 2014 11:39 UTC](https://forum.dynare.org/t/model-comparison-bayesian-estimation-again/3804/1 "2014-04-02T11:39:50Z")

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Hello,  
I read the relevant forum topics on this already, still not 100% sure I am doing it right, so some short **questions** are remaining:

I am basically comparing one model in 2 different versions, the second version differs only in one equation of the model block. I am using the same data for both models. I am using the same priors for the two models (althought the second model does contain additional parameters that didnt have to be estimated in the first - **(1)** does that bias the posterior odds ratio/model comparison decision somehow?)

Based on the formular of the posterior odds ratio, since I am using same data and priors, it is enough to compare marginal densities, right? ( **2** )  
\*\*(3) \*\*From th estimation results: “Log data density” was computed using ModifiedHarmonicMean and “Log data density [Laplace approximation]” via Laplace, correct?

The decision rule for model comparison using marginal densities is: **(4)** higher = better, correct? I.e. model A: -950, model B: -1000 ==\> choose model A.  
Or do I need to make any transformation on these values first (like taking absolute values, …) ? **(5)**

Thank you for your help !!

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