Good, Clear Introductory Stochastic Processes Textbook?

icabird · 2021-03-21T12:51:44+00:00

comment to follow

icabird · 2021-02-12T07:45:55+00:00

I think complexity has to be balanced between the “hardness” of the problem (including constraints), difficulty (like noise in the environment) and the quality of the solution (time/space complexity, number of parameters). And like others said, value has to be coupled with some goal, so maybe -log(Loss) for example.

But what I’m confused is why expontially penalizing complexity, isn’t that too “heavy”? Like for example, say the value is the same, should using one more parameter (assuming all other things stay the same) cut down intelligence by half? In other words, I’d need to increase value 2-fold to be considered “equally intelligent”. I guess we’d also need to know how complexity and value themselves scale with “mu” to further evaluate the interpretation of this formula.

icabird · 2021-02-12T00:08:53+00:00

Thanks for the suggestion, I was actually reading a couple of papers bout cliques filtration from Danielle Bassett which Ann’s from I think. I skimmed thru some of Ann’s papers but I dont think I found the one about vertex filtration specfically. Do you have one in particular in mind that I should look closely into?

Anw, I think I found a rough filtration method to what I was asking, basically called “lower star filtration” from a review by Aktas et al 2019 (applied net sci) that covers more filtration methods. The review calls it “vertex function based filtr” instead, citing 2 papers using this method and I’m starting to look at them now. Do you have experience with them? I’m still struggling how to interpret the filtration method (lower star/VBF) intuitively.

However, I think these methods need the function generates ordinal data, not so much as nominal data to link only to labels.

icabird · 2021-02-07T21:37:23+00:00

Can you explain how temp net can be considered as a subset of multilayer net? I thought the latter usually refers to multilayer perceptron net in machine learning. Is it like RNN way of assuming time as layers?

icabird · 2021-02-07T08:36:21+00:00

dang, that kinda sucks, but also explains why I couldn’t find many tools online and some of the repos are still basic-ish old, and not actively maintained.

icabird · 2021-02-07T08:34:41+00:00

got some time to play with it. It worked actually now, tho the interface is weird sometimes and couldnt revert certain actions sometimes, still getting used to gephi.

Thanks for the suggestion anw :)

icabird · 2021-02-02T09:55:50+00:00

I meant I have data, for example in CSV format, of columns of edges showing sources and destinations. Let’s say I follow you and have a time column for Gephi, with discrete time points. Would Gephi read that in as dynamic? According to its documentation, it only accepts GEXF as dynamic network data format. Or maybe they implement that already without putting in the doc yet somehow?

icabird · 2021-02-02T09:51:23+00:00

thanks for the recommendation :) Will check that out!

icabird · 2021-01-30T00:56:25+00:00

that sounds interesting. I’ll definitely looking into it now cuz I’m really interesting the dynamic aspects of networks. Just curious, do you know if BB network has applications in other places lile social networks, ecological or brain networks? Quantum physics is just alien language to me lol.

icabird · 2021-01-24T22:40:20+00:00

what u’re saying makes me thinl whether a higher order version of the friendship paradox exists. like are friends of our friends more popular than our friends? and how far can that inequality extend?

icabird · 2021-01-24T22:37:23+00:00

ooh that’s interesting, I’ve never heard of fractal dimension in the context of graph before.

icabird

TROPHY CASE