Hi Tiago, yes, I mean edge-covariates. In the example you referenced you compare state.entropy() for two distributions, i.e. exponential and log-normal, where for the log-normal model the covariates were scaled, which is handled by subtracting log(g.ep.weight.a).sum().
In case I want to simply compare two models with unscaled discrete covariates: one using a geometric distribution and one using a binomial distribution. Can I perform model selection by simply comparing their state.entropy() values? Best Regards, Enrique Castaneda El lun, 30 nov 2020 a las 13:45, Tiago de Paula Peixoto (<[email protected]>) escribió: > > Am 30.11.20 um 10:29 schrieb kicasta: > > Hi all, > > > > I´d have a question regarding model selection with different distributions. > > When we want to decide the partition that best describes the data for a > > given distribution we go with that that gives the smallest entropy. However > > say we want to compare 2 different distributions d1 and d2 and the best fit > > for d1 gives an entropy value of e1 and for d2 e2 respectively. If e1 < e2, > > can we say that d1 describes better our data than d2? > > Could you be more specific about to which "distributions" you are > referring? Are you talking about edge covariates? > > If so, model selection is explained here: > > https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#id28 > > In this case, the entropy* itself is not enough, you have to consider > also the derivative terms, as is explained in the above. > > (The term "entropy" is actually misleading in this context, since the > value refers to a log-density rather than a log-probability.) > > Best, > Tiago > > -- > Tiago de Paula Peixoto <[email protected]> > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool _______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
