There's no such thing as an "immune booster". The immune system is incredibly complex and nuanced, and there isn't a knob (or 10) that you can tweak to turn it up or down. It exists in a continuum of largely understood configurations, and it can learn and adapt (not always with good results). If anything, "boosting" it as in turning up immune activity is usually terrible (think auto-immune disorders, inflammation, cancer).
The "misinformation" people are battling is about a) unsubstantiated health claims and b) the fact that supplement manufacturers don't need to provide any evidence or follow any regulations regarding what's actually in the supplements. That means they can say they're selling e.g. Vitamin C but it's just sugar.
Try therapy and, if necessary, medication. Both of those have completely turned my life around. Things are still shitty sometimes (esp. during Covid), but way better than a few years ago.
The classification is done because rogue planets etc. tell us things about the process of solar system and planetary formation. Yeah nature tends to have a continuum of things, but the classifications are primarily used as place holders so that people know what everyone's talking about.
Since stars form in explosive events, it seems painfully obvious that some of the shit that comes flying out does not stick around, and that it could be planet sized/shaped.
Stars are formed in nebulas of gas and dust via gravitational collapse (accretion).
The remaining gas and dust that is swirling around the star flattens out into a disk (because of angular momentum). Planets gradually form in this disk, again via accretion (snowball effect).
I think emergence and complexity are important topics here. It's important that we actually understand how the pieces come together though. Maybe that's a place for functional programming/logic and frameworks that make composition easy
Something I've had trouble finding resources for is how to apply probabilistic/Bayesian techniques and thinking to chaotic dynamical systems. People keep telling me "Look up MCMC" but I don't see the relevance to dynamical systems (further than the notion that you can maybe sample from them with MCMC somehow?)
If all you want to do is sample from possible outcomes of the system, then you should just be able to run a simulation of it on your computer.
If you want to condition on an event? Say you want to predict the weather on Tuesday, conditioned on the event that it rained on Sunday? Run a lot of simulations, and only keep the ones where it rained on Sunday.
Similarly: If you want to compute an expectation value? Run a lot of simulations and take the average.
If the event is unlikely? Say you want to condition on the fact that it rained on Sunday, and the high temperature was precisely 15 degrees C? Then you have a difficult problem on your hands.
(Sometimes the expectation value of the quantity you care about will depend a lot on a few rare events with outcomes many standard deviations away from the mean. Then you also have a difficult problem on your hands.)
Sometimes MCMC will work on this kind of problem, and sometimes it won't. Even if it doesn't, maybe other techniques will work.
To apply MCMC to a dynamical system, one method is write down all of the history of the system as a single object, say a single vector. You write down what your system is doing at t=1, at t=2, etc, and all that information goes into the vector. The rules governing the system determine a probability distribution over the vector space that the vector lives in. (Or more generally, the object in the object space. The vector axioms aren't important here, it's just a nice familiar example.)
Generally speaking, if you know how to describe your dynamical system, you know how to compute an non-normalized probability for any given vector. Usually you won't be able to compute a normalized probability for that vector. That's fine, since MCMC works with non-normalized probabilities.
The next step is simply to run MCMC. If you want to condition on some fact, cut out all the parts of the space where that fact doesn't hold, and then run MCMC. If you want to compute an expectation value, there are other tweaks to MCMC that are possible (i.e. importance sampling).
Wonderful!!! Can you point me to any textbooks/resources that go into more depth on this (or if that's too specific, dynamical systems in general at the early graduate level)?
Shameless plug: I wrote some high quality (I hope) notes on HMC, because I couldn't find an explanation that was (a) rigorous, (b) explained the different points of view of MCMC, and (c) was visual! Here's the notes: https://github.com/bollu/notes/blob/master/mcmc/report.pdf
Feedback is much appreciated.
See the author's comment from a previous discussion:
"ClojureScript tries to be Clojure, and Parenscript tries to be Common Lisp. Eslisp tries to be JavaScript; it's intended to just be an obvious one-to-one syntax replacement.
I am definitely appreciating eslisp's almost one-to-one mapping to JavaScript. Finally I can program JavaScript with macros! I've searched for something like this before but didn't find it unfortunately.
Hy got my attention a while ago but I haven't played with it yet. Would you find yourself turning back to Python due to missing/annoying features? And is there an easy way to turn your code into a Python script so your co-workers can read it? :D
Leisure time etc. is very important to creativity and productivity as well. There's a lot of studies that I'm too lazy to link to, but you can find them pretty easily.
