AIC & BIC number interpretation I am looking for examples of how to interpret AIC (Akaike information criterion) and BIC (Bayesian information criterion) estimates. 
Can negative difference between BICs be interpreted as the posterior odds of one model over the other? How can I put this into words? For example the BIC = -2 may imply that the odds of the better model over the other model are approximately $e^2= 7.4$?
Any basic advice is appreciated by this neophyte.
 A: $AIC$ for model $i$ of an a priori model set can be recaled to $\mathsf{\Delta}_i=AIC_i-minAIC$ where the best model of the model set will have $\mathsf{\Delta}=0$.  We can use the $\mathsf{\Delta}_i$ values to estimate strength of evidence ($w_i$) for the all models in the model set where:
$$
w_i = \frac{e^{(-0.5\mathsf{\Delta}_i)}}{\sum_{r=1}^Re^{(-0.5\mathsf{\Delta}_i)}}.
$$
This is often refered to as the "weight of evidence" for model $i$ given the a priori model set.  As $\mathsf{\Delta}_i$ increases, $w_i$ decreases suggesting model $i$ is less plausible.  These $w_i$ values can be interpreted as the probability that model $i$ is the best model given the a priori model set.  We could also calculate the relative likelihood of model $i$ versus model $j$ as $w_i/w_j$.  For example, if $w_i = 0.8$ and $w_j = 0.1$ then we could say model $i$ is 8 times more likely than model $j$.
Note, $w_1/w_2 = e^{0.5\Delta_2}$ when model 1 is the best model (smallest $AIC$).  Burnham and Anderson (2002) term this as the evidence ratio.  This table shows how the evidence ratio changes with respect to the best model.
Information Loss (Delta)    Evidence Ratio
0                           1.0
2                           2.7
4                           7.4
8                           54.6
10                          148.4
12                          403.4
15                          1808.0

Reference
Burnham, K. P., and D. R. Anderson.  2002.  Model selection and multimodel inference: a practical information-theoretic approach.  Second edition.  Springer, New York, USA.
Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, USA.
A: I don't think there is any simple interpretation of AIC or BIC like that.  They are both quantities that take the log likelihood and apply a penalty to it for the number of parameters being estimated.  The specific penalties are explained for AIC by Akaike in his papers starting in 1974.  BIC was selected by Gideon Schwarz in his 1978 paper and is motivated by a Bayesian argument.
A: You probably use the BIC as a result of approximation to Bayes factor. Therefore you don't consider (more or less) a prior distribution. BIC in a model selection stage is useful when you compare the models. To fully understand BIC, Bayes factor I highly recommend reading an article (sec. 4): http://www.stat.washington.edu/raftery/Research/PDF/socmeth1995.pdf
to supplement knowledge with: http://www.stat.washington.edu/raftery/Research/PDF/kass1995.pdf
