## Functional Principal Component Analysis

IntroductionFunctional Principal Component Analysis (FPCA) is a generalization of PCA where entire functions act as samples ( over time a interval ) instead of scalar

## Resource Constrained Scheduling

IntroductionScheduling constrained resources over time is a tough problem. In fact, the problem is NP-hard. One part of the problem is to find a feasible

## Solving Ordinary Linear Differential Equations with Random Initial Conditions

IntroductionOrdinary linear differential equations can be solved as trajectories given some initial conditions. But what if your initial conditions are given as distributions of probability?

## Bayesian Regression Using MCMC

IntroductionBayesian Regression has traditionally been very difficult to work with since analytical solutions are only possible for simple problems. Hence, the frequentist method called “least-squares

## Progressive Self-Exploring Design of Experiments

IntroductionIn a classical design of experiments (DoE) you usually choose a set of points according to some rule and perform experiments to be able to,

## Introduction to Regression Using Gaussian Processes

IntroductionWhen trying to describe data using a function you often know something about the process generating the data a priori. When you do not completely