Tag Archives: Math

Confidence Intervals in Python

Suppose you are interested in finding out the mean weight of all Sumo wrestlers in Japan. Or the average gas consuption of Korean made automobiles… Why…? No idea, but that sort of statistics might be of interest, for someone, at … Continue reading

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Central Limit Theorem illustrated

A fundamental theorem of statistics is the Central Limit Theorem, which basically states that the averages (means) of a large number of samples drawn from any population converges towards a normal distribution. The graph above demonstrates this theorem: the blue … Continue reading

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The bat and the ball…

A classic from Behavioral Economics: “A bat and a ball together cost 1.10$. The bat costs 1$ more than the ball. How much does the ball cost?” The BE-guys have found that most people get this wrong. For the mathematically … Continue reading

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MH370 and Bayesian reasoning: example of SAR optimization

In two previous posts, [1,2] I’ve covered the fundamentals of Bayesian probability theory.  The second post looked into how the air distaster investigation team might have proceeded, once they received Inmarsat’s satellite data, to assess the likelihood of the flight path … Continue reading

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Bayesian reasoning and SAR for flight MH370

I covered the basics of Bayesian reasoning in a previous post. So, let’s apply Bayesian reasoning to the search & rescue operation of flight MH370. As we now know, Inmarsat’s identification of the most likely path taken by MH370, by … Continue reading

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Bayesian reasoning made simple by Golden Retrievers and other canines

    Just finished reading Brian Clegg’s excellent ‘Dice World – science and life in a random universe‘. Highly recommended to anyone who’s interested in the inherent unpredictability of our world, and even more highly recommended to all those empty … Continue reading

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image processing – convolution explained.

The Python code below demonstrates convolution.  First, using Scipy’s convolve() function, then by two – far from optimal, far from high performance!  – ‘invented-here’ implementations of convolve(), that I hacked in order to understand what Scipy’s convolve() actually does. The … Continue reading

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Optimization: 100x performance boost (even without DevOps!) :-)

(First of all, apologies for the mention of ‘DevOps’ above:this post has, as far as I can tell – since I really don’t know what DevOps is, haven’t found any place where to download it… – nothing what so ever … Continue reading

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Algorithm analysis – Big O notation

Continuing from my previous post on performance analysis of algoritms, I decided to plot the CPU-time over the size of the problem, that is, the number of cells. The two graphs above, both plotted ‘log-log’, demonstrate a straight line.  A straight … Continue reading

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Python – plotting with different scales

Ever wondered whether the data set you are dealing with is governed by a power law, or by some other distribution…? A quick and easy way to assess whether your data conforms to a power law is to plot the … Continue reading

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