Am 30.11.20 um 22:39 schrieb Enrique Castaneda:
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?
Yes, in the
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
Ok, but then I should be able to find the edge's target position in the
points generated by the the function get_hierarchy_control_points right? If
it is not the last two components of the vector (boundary condition?) it
should at the position [-4:-3]?
If I understand correctly what you said, from
Am 30.11.20 um 19:08 schrieb AntoineR:
Ok, that seems right, but still, there is something that I do not understand.
I followed the piece of code of the documentation of
graph_tool.draw.get_hierarchy_control_points
Ok, that seems right, but still, there is something that I do not understand.
I followed the piece of code of the documentation of
graph_tool.draw.get_hierarchy_control_points
Am 30.11.20 um 18:38 schrieb AntoineR:
Hi Tiago,
Sure the documentation states that there should be 6 points, but in my case
the length of the map is variable, so I think that either the documentation
is outdated, or the format of the map is not correct. Sometimes I have a
length of 40 for the
Hi Tiago,
Sure the documentation states that there should be 6 points, but in my case
the length of the map is variable, so I think that either the documentation
is outdated, or the format of the map is not correct. Sometimes I have a
length of 40 for the vector, sometimes 50 or so.
Thanks,
A.
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
Am 25.11.20 um 18:38 schrieb AntoineR:
Hi,
I would like to know also how the info are formatted... In my case the
length of the property is not the same across the edges...
The documentation of graph_draw() for "edge_control_points" says:
Control points of a Bézier spline used to draw the
Am 24.11.20 um 05:34 schrieb sam:
hi,
i think i understand the difference between the overlapping SBM and
mixed-membership SBM (introduced by Airoldi et al, 2009).
in MMSBM, a given node can be in multiple blocks, their membership is
discrete (0-1)
in overlapping SBM, a given node can also be
Am 21.11.20 um 02:25 schrieb jms:
Hi, I came across this library looking for an efficiently implemented graph
library and it looks awesome. One of the tasks I'd like to perform is
efficient edge contraction, where I remove an edge from the graph and merge
the two nodes joined by that edge, and
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
12 matches
Mail list logo
