- cypherpunks - lists.cpunks.org

[ot] [gmail] [log] [wrong] was Re: agent grarpamp 'predicts' the poronga virus PSYOP
by Karl 01 Jun '21

01 Jun '21

My personal experience is of just hitting "reply" on my phone. Sometimes the emails come from gmkarl, sometimes from 0xloem. I think i'm actually subscribed to the list under both emails. But I'm using my gmkarl account. I have gmail configured to reply with the address the email was sent to (it puts the account address in a header when it does this). I guess gmail must be picking using some implementation quirk, whether to reply as gmkarl or xloem, to emails sent to one or the other. I try to pick gmkarl instead of xloem when I remember on this list, so I can try to act less crazy when speaking as xloem, which is one of the email I addresses that i have more software development history as.

1 0

Population estimates from samples
by Greg Newby 01 Jun '21

01 Jun '21

Karl had been asking about how to estimate population statistics from a sample. I came across a fascinating approach to this, called bootstrapping or the bootstrap method. I learned about this an EdX data science course offered by Berkeley. The method is described in the course's free online textbook: https://inferentialthinking.com/chapters/13/2/Bootstrap.html This method makes some assumptions, including that your sample is reasonably large, and that the population distribution is approximately normal. The basic approach is to take your sample, and then randomly re-sample from the sample. This lets you build up a probability distribution of samples which, in turn, is representative of the population. The text includes some worked examples. The course uses Python (Jupyter notebooks) and a computational framework based on Pandas. The course sequence: https://www.edx.org/professional-certificate/berkeleyx-foundations-of-data-… It's the second course, "Inferential thinking through simulations," which introduces and builds on the bootstrap concept. Lots of the materials are free - including the computational framework and examples. I'm not sure whether you can audit the (self-paced) course for free. I found the bootstrap method to be very interesting. It is not something that came up in my many grad and undergrad statistics courses or other research methods (maybe it emerged since I was a university student). I hope this helps. Greg

3 2

german data on the fake flu farce
by Punk-BatSoup-Stasi 2.0 31 May '21

31 May '21

attached, for people like grarpamp who don't know how to 'surf the web' As can be seen, during most of 2020 there was virtually no poronga virus, and then the german govt started lying, around week 45. STILL, the peak at end of 2020 fraudulently atributed to the poronga virus is no different than the peak in week 10 year 2018...when the poronga virus PSYOP didn't exist. In other words, for people who have trouble reading a basic chart, there are no 'excess deaths' above the 'natural' variations.

1 0

[wrong] [random] [ot] [keywords in brackets]
by Karl 31 May '21

31 May '21

It was _so hard_ to do that probability calculation! Omigod! My brain is all confused and I _succeeded_! It totally works on live data I just need to go through and fix edge cases. The first one I poked at was handling the impact of data from outside the histogram range. Basically it gets totally wrong results if that stuff isn't modelled or avoided. My project is now somewhat interwoven with posting to this list in my mind, which is a little irritating, so trying to reduce that. Pushing hard through an inhibition can cause me a huge variety of weird experiences including forgetting where I am and what I am doing, having weird beliefs that I am in the middle of other unrelated tasks, etc etc. As I push more frequently those experiences shift and can improve, but there's a certain degree of pushing that can do just about anything to your brain, and I may have hit that again, kinda. It gets better with practice. It's confusing! It's so satisfying to have reached that goal, but I'm not sure how to continue after it. My habits for doing something I choose are all a little injured from the behaviors involved in reaching it.

1 4

Forbes: Google Suddenly Flips The Password Privacy Switch For Billions Of Users
by jim bell 31 May '21

31 May '21

Forbes: Google Suddenly Flips The Password Privacy Switch For Billions Of Users. https://www.forbes.com/sites/daveywinder/2021/05/30/google-suddenly-flips-t…

1 0

Fox Business: Google employees shared concerns over location tracking, Arizona lawsuit reveals
by jim bell 31 May '21

31 May '21

Fox Business: Google employees shared concerns over location tracking, Arizona lawsuit reveals. https://www.foxbusiness.com/politics/google-location-tracking-employee-conc…

1 0

You need to watch the most thrilling cyberpunk movie on Amazon Prime ASAP
by jim bell 31 May '21

31 May '21

https://www.inverse.com/entertainment/science-fiction-movies-amazon-prime-m…

1 0

[wrong] [ot] crazy journal
by Karl 30 May '21

30 May '21

Maybe wrong is a reasonable replacement for spam. Not using my spam tag seems a little worrisome. Wrong might call out my attention to not use it if what i'm saying is clearly right. I worked on gnuradio blocks for a bit! Yay! Radios are exciting. I'm stuck around my cognitive inhibitions with a stats problem. I'm trying to estimate which among a set of populatiom histograms a sample is most likely to fit, and I keep on freakin' out trying to make my brain do it rightly. Really i'd like to give a number to each option, so the user had an idea of how certain to be, and about what. I have two half-clang heuristics. One is to use a bernoulli distribution for each bin to give the portion of samplings that would be a worse guess for the bin, and take the product of that metric for all the bins. I don't remember it well: it gived a nice similarity metric from 0%-100%. The other half-clang heuristic is a matrix solution. I make a matrix out of all the population histograms, and solve for what vector multiplies them to get the histogram in question. This comes out with a nice result where 1.0 occupies choices that are precisely the same, but makes incredibly poor guesses for situations where there is no really good choice. I made the heuristics while playing around with actual probability and statistics, but it isn't quite cognitively working for me. At this point I can barely think about it! I can barely review my existing heuristics, even. Ideally, I'd like to output the actual probability of each histogram, being the one among the set, that the sampled histogram was sampled from. I've never taken many probability or stats classes, and the few I did take seemed to be just parroting things that were already obvious, so I didn't attend to them well, and don't have much experience or training with these things. When I google things like this, it usually tells me to go through a song and dance involving confidence intervals and significance and such. These are things I value, but as I guess I was a hacker I really value understanding the things I use, and picking the best solution based on that understanding. One thing i've noticed that makes it a little easiet for me, is that stats descriptions can leave out the properties they are describing the statistic of, which can make it more confusing. The probability of something happening is different than the probability of your guess about it happening being right, which is different than the probability of it happening if undescribed information about it is known, etc etc. A stats page might mention the distinction once and then assume everyone remembers, and that's hard for me nowadays. Notably, the probability of something happening given data, is different from the probability if it happening in the real world. That's confusing to me. It could be fun to model everything I measure to give it a good prior thingy, but that's not even what I want: I don't want to know what is most likely overall, I want to know what the data indicates is most likely. I'm not totally sure how to think about that, but the fundamental concept of summing favorable events and dividing them by total outcomes clearly assumes a uniform distribution of outcomes, and my brain does that too when I consider things around me. If you want to make a fair comparison of things, assume they have a uniform distribution so the comparison acts fairly. I don't really know. So, if we want to figure out the likelihood of one histogram being made from another among a set of distribution histograms, we could obviously enumerate all possible histograms that could be sampled from any one among the set, count how many are the same as one we sampled from each, and divide the number for the one in question by the total that are the same. I think I'm leaving something out there, statistically, when I consider it, but it seems like a helpful grounding point. The goal is clearly possible. With small histograms and relatively few samples, it would even be possible to simulate the above brute-force solution, to give the probability a histogram fits among a set, chart some data along every variable, and empirically derive an equation of probability. But it seems like such a basic thing that, had I the education, I expect there would be some formula solution one would know from the problem.

1 28

[wrong] [random]
by Karl 30 May '21

30 May '21

The confused hypnotist sat down with the urgent victim. "Mind control me to have my life destroyed! Please please please please!" the urgent victim said to the hypnotists. "Uhhh . . ." said the hypnotist.

1 2

Phys.org: Breaking the warp barrier for faster-than-light travel
by jim bell 30 May '21

30 May '21

Phys.org: Breaking the warp barrier for faster-than-light travel. https://phys.org/news/2021-03-warp-barrier-faster-than-light.html Jim Bell's comment:. Not quite 'Star Trek' level, but...

2 1