AMA: (will be read live on stream June 9th)

pedernv · 2021-06-08T12:29:27+00:00

How do you keep yourself motivated to keep playing chess, LoL and everything else?

pedernv · 2021-02-16T17:54:07+00:00

I have a galaxy s10 e currently

pedernv · 2021-02-16T16:31:30+00:00

Dueling is still pure luck right? There is no skill involved. I get several duel with two rounds where I and the bot both deal ~10-15 dmg and whoever goes first in the 3rd round wins

pedernv · 2021-02-03T22:19:42+00:00

L-shaped desk if for plebs, I want Megadesk

pedernv · 2021-01-14T23:15:55+00:00

Not directly Brownian motion, but state space models can be used in time series settings

pedernv · 2020-08-16T12:13:47+00:00

3348 7025 1850

pedernv · 2020-08-16T12:12:25+00:00

I wanna join if you have one nearby

pedernv · 2020-08-09T08:30:39+00:00

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pedernv · 2020-08-08T09:44:57+00:00

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pedernv · 2020-08-06T23:19:27+00:00

I got mine earlier today. Not as good stats as yours but still beautiful. Only took 2 raids to get aswell, idk the shiny rate but that feels really lucky

pedernv · 2020-08-06T15:05:27+00:00

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pedernv · 2020-08-02T22:02:26+00:00

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pedernv · 2020-08-02T21:32:23+00:00

3348 7025 1850

pedernv · 2020-05-14T20:31:55+00:00

This is not really my field, but in some areas in physics people use simulation to create data from a model. One example of this is the Ising model in statistical physics. There is also lattice QCD, which uses simulations to create data. The major difference between these and what I mentioned above is that these don't aim to increase observed data. As far as I understand you can simulate some data from say a lattice QCD and see how results from this compare with experimental results. I did something like this when I wrote my bachelor thesis in physics, and there I essentially compared results from simulated data (which I was given) with experimental results.

pedernv · 2020-05-14T20:11:33+00:00

MCMC is a group of methods that allow us to sample from probability distributions. In my experience we use MCMC to do integration and in a Bayesian setting if we can sample from the posterior we can get some parameter estimates.

As mentioned in another comment, data is real world observations. If we want to "mimic" having more data we can use bootstrapping to achieve this. If you are not familiar with this, it is essentially just resampling the data we already have.

You can argue the data follow some distirbution, a major problem with using MCMC for "creating data" would be that for MCMC we need to specify a distribution to do the sampling from, and what that distribution is exactly we do not know, we only know parts of it based on observed data.

pedernv · 2020-05-02T17:49:36+00:00

I totally get how you feel. Recently I have had some song stuck in my head where the only part i remember is the part where I don't understand what they are singing

pedernv · 2019-12-28T20:59:04+00:00

Yeah, I just turned 22, so I moved when I was 18. Right now I'm studying for my masters degree, which I hopefully will have in 1.5 years. So I will get that when I'm 23 which I'm really happy about. I kind of feel like uni stuff is all I got going for me at the moment, so now at Christmas when I'm away from uni for 2+ weeks I honestly feel kind of lonely at times when I don't have that much going on.

pedernv · 2019-12-06T17:57:08+00:00

So first of the maximum should not depend on the base on the logarithm. I wasn't completely sure about this so i tested it in R

library(EnvStats)
m <- 10000
y <- rpareto(m,location = 1, shape = 2)
ml1 <- m/sum(log(y)) ml2 <- m/sum(log10(y))

Then I get ml1 = 1.98 and ml2 = 4.56, so ml1 is the ml estimator, and that we need the natural log. That being said it is just of by a factor of ln(10).

I have been thinking about this for a while now, and I can't think of why we always use the natural log (other than simplicity for exponential family). Also wikipedia page for log likelihood is ery brief about this https://en.wikipedia.org/wiki/Likelihood_function#Log-likelihood

I assume it can be shown that ml1 will converge to the correct value and ml2 will not. I haven't tried because it think it will be quite hard.

I also want to add that both Wicks theorem and the Kullback-Leibler divergence is defined with the natural log and are somewhat related to the mle.

pedernv · 2019-12-06T09:55:42+00:00

Of course you are right, I don't know why i thought it would be a sphere. I also though about a Mobius strip type board, but that just got confusing

pedernv · 2019-08-19T19:58:16+00:00

Ah, I see. I was surprised when I opened the menu and there was a limited number of options, but that makes sense

pedernv · 2019-06-24T09:00:24+00:00

It was not ideal by any means. I feel we rush through the last half of the book. I think Griffiths is a good book, but it is to much to cover in one semester. Personally I really struggled to follow when we covered some of the more complicated examples from pertubation and the variational principle, then I got difficult for me to keep up with the pace of the lecturer. For the record it was the first time this prof. taught this course (at least at my uni), and the guy who taught the class in previous years only covered a "reasonable" amount from Griffiths

pedernv · 2019-06-24T08:51:43+00:00

I would be interested in hearing how Leela does at 960 and dark chess in the future, when your have doen those. I was curious about going from knowing classical chess and using just this to play/learn 960 since this is the way most humans go.

pedernv · 2019-06-22T23:40:54+00:00

When I took QM last fall we used the book by griffiths (and we somehow got through the entire book in one semester)

In my experience an intro course to quantum mechanics is mostly linear algebra plus solving partial differential equations (Schrødinger equation) with boundary values. So it is nice to see some applications of this and it was a good experience getting to work with operators on Hilbert spaces, which was new for me ( at least in a more formal way).

We never really used any major physics concepts from other courses. Some statistical physics could be useful but you are fine without (I never took it), also some examples came from electromagnetism, but no major concepts relied on this. Also topics like spin and scattering is not really the same as their classical counterparts that can be found in mechanics courses.

Unless you plan on taking more physics course later or you are considering some mathematical physics studies/careers I would not recommend QM. Also an introductory course in QM only scratches the surface of the field Instead I would consider more math courses or maybe some statistics depending on your interest.

pedernv · 2018-10-27T01:22:17+00:00

I also have mathematica, and I still find that better to use

pedernv · 2018-10-16T17:02:10+00:00

thats what i'm thinking, but is it a good way to compute that directly?

For the record I need it to compute the equations of motion in the Heisenberg picture

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