A science blog about my spare time data analysis projects.
Efficient data processing lies at the heart of modern machine learning. Kernel smoothing, a versatile non-parametric technique, benefits significantly from performance improvements through optimized algorithms and frameworks. In this post, …
Introduction In GLMs, we often encounter scenarios where we need to account for exposure or adjust for certain factors. Both offset and weights play crucial roles in achieving this. Let’s …
Predicting stock prices is akin to the attempts of alchemists in the Middle Ages to transmute lead into gold. Just as alchemists sought to unlock the secrets of transformation, statisticians …
Problem Statement Let thus , we construct a stochastic process as , this kind of random variable are called hypoexponential random variables. We define a counting process such that . …
In many applications actuaries, data scientists or researchers are confronted with datasets like shown in Table 1. While nominal variables, as in the first or second column, can be used …
I recently realized that I have a certain habit when it comes to stock trading even though I used to tell myself that “every trading strategy is useless because the …
Estimating maxima and minima of a noisy curve turns out to be very hard and to a large part is still an open question. In this blog post we will discover some strategies …
Working at my post about the dask cluster, I realized that the code snippets presented in the post will eventually change in my GitHub Repo. I wanted to avoid having …
In this article we will show a way to do high performance parallel computing at a Kubernetes cluster using task. A primary focus is that we want to archive the …
This post is supposed to give a brief introduction in Frequency-Severity models. These models are very popular for determine the optimal price for an insurance. We will take a look …
1. Setup In a previous post it was shown how to speed up the computation of a kernel density using the Fast Fourier Transform. Conceptually a kernel density is not …
In the articles Kuberentes at an OrangePi and Setting up the OrangePi it was described how I build my toy cluster. Meanwhile the cluster received some updates. Two more nodes …
1. Setup This Post is about how to speed up the computation kernel density estimators using the FFT (Fast Fourier Transform). Let be be a random sample drawn from an …
In a previous post I covered how to apply classical linear estimators like support vector machines or logistic regression to a non-linear dataset using the kernel method. This article can …
1. Install k3s at the OrangePI In a previous article I explained how to get spark running at an OrangePi to create a toy computing-cluster. If you look at this …
In the last two articles we covered the topics “How to teach a computer gamerules” and “The Multiarmed Bandit Problem”. Indeed these two articles where intended to be an introduction …
Sequential games with perfect information 1. A very short course in Game Theory In the twenties people start to describe games using math. Since then Game Theory becomes an important …
1. Formulation of the Multiarmed Bandit Problem Consider the following problem: A gambler enters a casino with slot machines. The probability to receive a reward for each slot machine follows …
To explain the registration problem i will start with an example. In Figure 1 the pinch force dataset is shown, to collect the data a group of 20 subjects were …
1. Introduction This blog post is about Support Vector Machines (SVM), but not only about SVMs. SVMs belong to the class of classification algorithms and are used to separate one …