bernoulli

Petersburg paradox was first proposed by Nicholas Bernoulli in 1713 and solved by Daniel Bernoulli in 1738.

Now, some answers to why wings work are just plain wrong (like the original wrong answer -- the Bernoulli effect).

The Physics of Wall Street covers the history of math from Bernoulli through Mandelbrot and how we arrived at modern algorithmic trading.

In this case, the probability measure is the Bernoulli measure.

In numpy: data = cumsum(bernoulli(p).rvs(1024*16)) test_statistic = special.xlogy(data, p) *...

> Now, some answers to why wings work are just plain wrong (like the original wrong answer -- the Bernoulli effect).

Yes, this is really how it works, and Bernoulli should be left in the dustbin, since in comparison it's a smaller effect, with more impact for carburetors than aircraft wings.

> The Physics of Wall Street covers the history of math from Bernoulli through Mandelbrot and how we arrived at modern algorithmic trading.

Might need a better specifed prior for that Bayesian calculation - prior expectation of 1% gets you the mean of the prior distribution of the success probability of each Bernoulli trial, but you also need a variance or other measure of the uncertainty of your belief.

If you model the two alternatives as Bernoulli processes with unknown success rates, and assume that the only difference between the two is what is specified on the page, and that they don't interact, and you assume a uniform prior on both parameters, B's conversion rate is higher than A's with probability 0.999572.

The end." Maybe I misread something, but nowhere in the examples that are apparently inconsistent with Bernoulli's law, and for which the professor claims they actually support it, does he actually provide an explanation.

To take an example from aviation, we didn't have practical simulations of general viscous fluid flow in the 1960s, but it would be meaningless to say that we "used Bernoulli's principle" to fly across the Pacific.

To do this, consider the random matrix t = Bernoulli(T) (i.e., T = E[t]) The distribution is m_n = M_1 t_1...

I understand Bernoulli's principle, and I also understand that a carburetor is lousy at part throttle metering.

Bernoulli's solution is interesting in that it introduces the concept of Utility, it isn't the most satisfying to me.