wolajacy

Moving Data Around is Slow

AFAIK, popular data science tools (Spark, Pandas, etc.) already use columnar formats for data serialization and network-based communication: https://en.wikipedia.org/wiki/Apache_Arrow

Similiar idea for disk storage (which is again orders of magnitude slower, so the gains in certain situations might be even bigger): https://en.wikipedia.org/wiki/Apache_Parquet

Generally, if you're doing big data, there are actually more benefits from using this layout - data homogenity means much better compression and possibilities for smarter encodings.

We Need Browsers as Platforms

Random users installing random software gives you botnets.

This is only true in case of insufficient security mechanisms. Virtualization/containerization (for example, docker model) would allow users to run independently installed applications safely.

Similarly, I guess that the motivation for centralized store (apart from the financial motive of the store owner: Apple/Google) is to provide security through the process of vetting the apps. But again, if we had proper virtualization software, there would be no reason not to allow users to add unofficial repositories, maintained in a decentralized way.

Of course, virtualization/containerization done on the OS level is (currently) quite resource-intensive. But the alternative is even worse - with everything moved to the web, we are building (we have built...) OS inside OS! With all the problems that it entails: this "new OS" supporting really only one language, having extremely limted set of protocols, overall not having anything close to the full environment of the proper OS.

Summarizing: why would you advocate this all just to solve intercompatibility and safety problems (which, if I read your post correctly, are the reasons for moving apps to web), instead of dealing with them properly, on the OS level?

Why not? potato chips in a box edition

I really like the thought behind the post! But, your idea seems kind of... overengineered. For one, an important requirement for the packaging is that it should be easy to hold in your hand (e.g. eating in a car/on a couch/anywhere that you can't actually put it on a table).

Additionally, let's say there are two varieties of chips' sizes: small and large. Small ones are small and cheap, so there's no better way to package them than throw some in a bag, and it'd be too costly to package them in a more sophisticated way.

Large ones could have more complex packaging, but there's the problem of closing the bag when there's still some leftovers. In case of the usual bag, it's as easy as folding the top - you get reasonable airtightness etc. But in case of a box, you'd have to make some closing mechanism, or shove it back in the bag (as in your pictures), which seems... complicated.

There are two ideas here. First are Pringles - just put them in a tube. Closing is not a problem, and it has the additional advantage of not crumbling them to pieces (which I'd say should be THE feature of boxes). Second idea is a bag that can be opened vertically as well as horizontally (Lay's Stix implemented this some time ago, although I'm not sure about the US version). Then, you can have best of two worlds - easy to hold/easy to close (open on top) OR easy to access/share (open on the side).

Where can I find good explanations of the central limit theorems for people with a Bayesian background?

If you don't have a given joint pobability space, you implicitly construct it (for example, by saying RV are independent, you implicitly construct a product space). Generally, the fact that sometimes you talk about X living on one space (on its own) and other time on the other (joint with some Y) doesn't really matter, because in most situations, probability theory is specifically about the properties of random variables that are independent of the of the underlying spaces (although sometimes it does matter).

Your example, by definition, P = Prob(X = 6ft AND Y = raining) = mu{t: X(t) = 6ft and Y(t) = raining}. You have to assume their joint probability space. For example, maybe they are independent, and then it P = Prob(X = 6ft) \** *Prob(Y = raining), or maybe it's Y = if X = 6ft than raining else not raining, and then P = Prob(X = 6ft).

Where can I find good explanations of the central limit theorems for people with a Bayesian background?

Answering the last question: If you deal with any random variable, formally you are specifying a probability space, and the variable is a measurable function on it. So, to say anything useful about a family of random variables, they all have to live on the same space (otherwise you can't - for example - add them. It does not make sense to add functions defined on different spaces). This shared probability space can be very complicated by itself, even though the marginal distributions are the same - it encodes the (non-)independence among them (in case of independent variables, it's just a product space with a product measure).

Where can I find good explanations of the central limit theorems for people with a Bayesian background?

Don't have any good source except univeristy textbooks, but:

- The simplest proof I know of (in 3 lines or so) is to just compute characteristic functions.
- In general, the theorem talks about weak convergence, i.e. convergence in distributions.
- The sample mean converges to expected value of the distribution it was taken from almost surely (i.e. strong convergence). This is a different phenomenon than CLT, it's called the law of large numbers.
- CLT applies to a family of random variables, not to distributions. The random variables in question do not have to be identically distributed, but do have to be independent (in particular, independence of a family of random variables is NOT the same as their pairwise independence).
- The best intuition behind the CLT I know of: Gaussian is the only distribution with a finite variance where a linear combination of two independent variables has the same distribution (modulo parameter shift) as they have (i.e. it is a
*stable**distribution*). So, if try to "solve" the recursive equation for the limit in CLT, you'll see that, if it exists, it has to be Gaussian. The theorem is actually about showing that the limit exists.

In general, as someone nicely put this:*The importance of stable probability distributions is that they are "attractors" for properly normed sums of independent and identically distributed (iid) random variables.*

Against Victimhood

On the exactly the same phenomenon, but from a different perspective - C. S. Lewis in The Great Divorce goes to explain the Christian Hell as the place that people are stuck in because they choose to wallow in despair/grief/anger/victimhood, instead of just forgiving and letting go.

For example, he talks about a mother that lost her child, and is now stuck on anger of this child being unfairly treated by the word/God. The crucial fact is that she is indulging in that anger as a way of signalling her own self-rightioussness, not for any productive purpose.

Quite interesting, how all these different worldviews converge on that one :)

Escalation Outside the System

This reminds me of the "The Toxoplasma of Rage" post by SSC: https://slatestarcodex.com/2014/12/17/the-toxoplasma-of-rage/

The question of "why do the left play into violent confrontation, even though it's suboptimal from their perspective" is another version of the central question discussed in the article.

What posts on finance would your find helpful or interesting?

1) Meta-level post listing interesting sources to learn different aspects: assets pricing, relevant parts of macro (for example, it seems that in academia, there's a number of different, conflicting, business cycle theories - but as financial institutions actually have skin in the game, it'd seem reasonable that they came up with a "correct" version of it), HFT, general info.

2) Examples of (obviously, already-dead) strategies that made significant profit before - if such descriptions are available anywhere.

2) Books reviews - having recently read Flash Boys and When Genius Failed, I'd be really interested in an expert evaluation of those. Also, other books recommendations (as in 1), but with a more documentary(or even fiction?)-focus.

There is a great (free) online course called 'NAND to Tetris', which is built on this exact premisse. Can't recommend it enough: https://www.nand2tetris.org/