Young-Jin,

Similar to AIC, BIC is nothing but a penalized version of loglikelihood.
There is no way to tell 'how small is small' for BIC unless you compare BIC
from one model with BIC from another model.


On 3/6/06, Young-Jin Lee <youngjin.michael@gmail.com> wrote:
>
> Dear R-List
>
> I have a question about how to interpret BIC as a goodness-of-fit
> statistic.
> I was trying to use "EMclust" and other "mclust" library and found that
> BIC
> was used as a goodness-of-fit statistic.
> Although I know that smaller BIC indicates a better fit, it is not clear
> to
> me how good a fit is by reading a BIC number. Is there a standard way of
> interpreting a BIC value?
>
> Thanks in advance.
>
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--
WenSui Liu
(http://statcompute.blogspot.com)
Senior Decision Support Analyst
Health Policy and Clinical Effectiveness
Cincinnati Children Hospital Medical Center

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