Two mariners report to the skipper of a ship that they are distances d1 and d2 from the shore. The skipper knows from historical data that the mariners A & B make errors that are normally distributed and have a standard deviation of s1 and s2. What should the skipper do to arrive at the best...
Someone (haruspex) on this thread mentioned the right thing. In the real world, those coins are not equally probable. Guess it all depends on whether the coins are selected at random or are they real "change" from transactions.
The roots of the gaussian distribution lies in the method of least squares, used widely in the 17th and 18th century for navigation, astronomy. To explain the method of least squares, arises the Gaussian distribution.
This is also known as the Monty Hall problem. Discussed here (http://bayesianthink.blogspot.com/2012/08/understanding-bayesian-inference.html)
Your calculation is correct. Basically in light of new evidence, it makes sense to change the choice of doors
From the graph it does look like there is a strong correlation. If you want to put a number on it, simply take the temperature points (controlling for seasons) and compute the mean/std. Next find the probability of finding those points from a gaussian. This will tell the likelihood of observing...
Quite frankly, no. Such a book wouldn't exist. The main reason here is that historically, historians (alliteration :) ) disagree quite a bit on origins of mathematics and what came from where. This is true even if we are talking civilizations (east vs west) or even whether it was Newton or...
Math & computer science absolutely. A focus on probability theory would do great. Some books & puzzles are here http://bayesianthink.blogspot.com/2012/12/the-best-books-to-learn-probability.html
Before you do numerical analysis you need to know some programming language & probably some basic calculus. Knowing some probability theory & optimization could also help. Some good books are here http://bayesianthink.blogspot.com/2012/12/the-best-books-to-learn-probability.html