Unlocking Stock Market Insights with RiskBERT

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 …

Generalized Semantic Regression using Contextual Embeddings

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 …

Guessing Passwords using an LSTM Network

Introduction A while ago I applied NLP strategies to implement an algorithm that is capable to guess a password. Since then a new method called transformer was developed and successfully …

Building a minimal, cost efficient Dask cluster

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 …

Frequency-Severity Modeling in consideration of COVID-19 induced effects

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 …

Kernel Regression using the Fast Fourier Transform

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 …

Fast Kernel Density Estimation using the Fast Fourier Transform

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 …

Non-Linear Classification Methods in Spark

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 …

An AI with less than 200 lines of code

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 …

Teaching a Computer Gamerules

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 …

Solving the Multiarmed Bandit problem with JavaScript

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 …

Non-Linear Support Vector Machines (SVM)

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 …

Kernel Regression using Pyspark

1. Kernel Regression using Pyspark In a previous article I presented an implementation of a kernel denisty estimation using pyspark. It is thus not difficult to modify the algorithm to …

Nonparametric Density estimation using Spark

1. A Nonparametric Density implementation in Spark One of my previous blog post concerns about nonparametric density estimation. In this post i presented some Matlab code. An advantage of this …

Functional Regression with Spark

1. Functional Regression Let the covariate be an at least twice continuously differentiable random function defined wlog. on an interval and the corresponding the response. For simplicity we assume centered …

Functional Principal Component Analysis with Spark

1.) Functional Principal Component Analysis Let be a centered smooth random function in , with finite second moment . Without loss of generality we assume instead of some arbitrary compact …

2. Coding the “Educated Guess Procedure”

1. Perform the Analyze To start with, we load the “rockyou.txt.tar.gz” password list using wget. I’m not sure if it is legal to provide a link for the list, therefore …

5. Running some tests

1. Test the Enviroment 1.1 Simulation of a Brownian Motion The purpose of the first notebook entry is to check if matplotlib is correctly installed. We simulate 20 Brownian Motions …

Kernel based Estimators for Multivariate Densities and Functions

1. Kernel Functions In general, a kernel   is an integrable function  satisfying is symmetric (e.g. if ) . Popular univariate () kernel functions: Uniform: Epanechnikov: Gaussian:  An easy way …

Estimating Multivariate Functions and Derivatives using Local Polynomial Regression in Matlab

1. Theoretical Background I want to start with some theory which will in the end lead us to the Matlab coding. If you are not interested how to derive the …