Life is complicated, you probably know that. If we take a magnifying glass and look at a living thing from a chemical and biological perspective, it is astonishingly complicated. In this blog post, I will walk through an example of a process that occurs in all living things and how we can study this process with a computer. In fact, I will … Continue reading Life in a computer
[redirect url='http://combine.se/the_vhil/' sec='0'] Since the introduction of separable software components and virtual testing, the development of software for mechatronic systems is taking place parallel to the procedure of producing the hardware. The progress has made it possible to shrink the time for development and also gain knowledge, through testing, at an earlier stage of the … Continue reading The vHIL – The new sibling in the loop family
[redirect url='http://wp.combine.se/the-next-level-of-virtual-verification/ ' sec='0'] This first blog post will be a brief survey about the use of virtual verification within the development of mechatronic systems. There will also be some considerations about future concepts within the field, which will give you some clues about possible topics for the upcoming posts. This time, we will consider … Continue reading The next level of virtual verification?
The maximum spacing estimation (MSE or MSP) is one of those not-so-known statistic tools that are good to have in your toolbox if you ever bump into a misbehaving ML estimation. Finding something about it is a bit tricky, because if you look for something on MSE, you will find "Mean Squared Error" as one of the … Continue reading Looking into Maximum Spacing Estimation (MSP) & ML.
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The Kolmogorov-Smirnov test (KS test) is a test which allows you to compare two univariate, continuous distributions by looking at their CDFs. Such CDFs can both be empirical (two-sample KS) or one of them can be empirical, and the other one built parametrically (one-sample). Client: Good Evening. Bartender: Good evening. Rough day? Client: I should … Continue reading Kolmogorov-Smirnov for comparing samples (plus, sample code!)
And here we go with the copula package in (the sandbox of) statsmodels! You can look at the code first here. I am in love with this package. I was in love with statsmodels already, but this tiny little copula package has everything one can hope for! First Impressions First I was not sure about … Continue reading Trying out Copula packages in Python – II
You may ask, why copulas? We do not mean this copulas. We mean the mathematical concept. Simply put, copulas are joint distribution functions with uniform marginals. The kicker, is that they allow you to study dependencies separately from marginals. Sometimes you have more information on the marginals than on the joint function of a dataset, … Continue reading Trying out Copula packages in Python – I
Every now and then, a data science practitioner will be tasked with making sense out of rare, extreme situations. And the good news is, there exist mathematical tools that can help you make sense of extreme events. And some of those tools are structured under a branch of probability which has (conveniently) been named Extreme Value Theory (EVT).
Embracing the turbulence