mvanaltvorst

Not necessarily if you get rid of the energy in another way, right? You could perform hydrolysis to turn water into hydrogen + oxygen, and if you release hydrogen into the atmosphere it will just escape our atmosphere into space.

It's naive to think you can compare vectors without any sort of prior on what this relationship might look like. Hoeffding's D cannot effectively capture relationships between time-shifted series that the (generalised) cross correlation can. Nor can it identify patterns in conditional heteroskedasticity. You will always need to impose some sort of prior when comparing vectors, and Hoeffding's D is rarely the best choice.

In my experience, PyMC leads to models that are orders of magnitudes slower than equivalent models written in JAGS. Profiling is also extremely tedious, and there is no section in the PyMC docs that touches upon model performance.

I really like PyMC's API, but as soon as you move towards bigger datasets JAGS or Stan seem to be the only practical options.

You might be fine with DisplayLink and two monitors, however in my experience DisplayLink with an M1 and four external monitors was nearly impossible to use because of the input lag. Sometimes the entire DisplayLink driver would crash. I bought an M3 Max instead, there is no robust alternative.

I immediately tried to find a comparison with ARIMA as well and was disappointed. It's difficult to take this paper seriously when they dismiss a forecasting technique from the 70's because of "extensive training times".

Newton's method is an algorithm you can use to minimise/maximise/find the root of a differentiable function very quickly if you start with a reasonable guess. There are many variants on Newton's method, e.g. BFGS that does not require the hessian (/2nd order derivative) of the function. A pitfall of Newton's method is that it does not find the global minimum per se, it might just converge to a local minimum if this local minimum is "closer" to your initial guess. Some variants of Newton's method allow you to avoid this pitfall, but they are slow in practice.

This seems to be another variant that claims to find the global minimum quicker than other existing methods. I am not experienced enough to verify this claim. I am also not sure how this new algorithm performs in practice; in theory, ellipsoid methods are a lot more efficient than simplex methods for optimising convex functions, while in practice simplex methods are usually an order of magnitude faster. So take this result with a grain of salt.

This sounds great, but how likely is it that the US will manage to reach 100$/GT?

Statisticians and mathematicians call them dimensions, machine learning engineers call them "features". Statisticians and mathematicians usually interpret observations as vectors in n-dimensional space, which leads to some interesting geometric results. Hence the term dimension, we do mathematics with them as if they were actual points in some high-dimensional space. Degrees of freedom are something a bit more nuanced that are of use when you start to do specific statistical tests, and they do not have to be exactly equal to your amount of dimensions.

Wonder if this can be used to bypass ImmersedVR's display limitations

The minute we have solved AGI, will we have solved economics as well? I would imagine that if you just pit 100,000 smart agents against each other with a virtual currency you could calculate the ideal government policies to maximise welfare.

Not true, the coil emits its own radio waves which are exactly 180 degrees phase shifted with respect to the radio tower. Destructive interference could be measured behind the coil, even when not exactly in its shadow.

Yes, this would work. Hard to reason about LP solvers in complexity notation, but would probably be pretty quick.

You could also construct a graph, where every price is a node. Start at 0.00, and do a BFS until you find the desired price. Worst case scenario O(n) where n is the desired price. Could be optimised by using a path finding algorithm like A*, its heuristic would make it try a greedy algorithm at first and then do a more complex solve at the end.

Another possibility is by memoization (dynamic programming). Strictly worse than the graph algorithm in complexity terms, but in practice your computer is very good at working sequentially on a very big array of booleans.

Really many different approaches to solve this problem. In this specific case greedy worked as well.