Life in a computer

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

The vHIL – The new sibling in the loop family

[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

The next level of virtual verification?

[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?

Kolmogorov-Smirnov for comparing samples (plus, sample code!)

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!)

Trying out Copula packages in Python – I

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

Sympathy for the Extreme

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).