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Feb 20,2023
Proven Tips for High-Performance Neural Networks for AI
Before using Neural networks, you should make sure that you need a complicated model. However, as far as you know that you need a non-linear decision boundary, a Neural Network may generally be a good…
Oct 10,2025
Case Study: Bayesian Structural Equation Models in Clinical Study Data with Small Samples
Overview Data and Context Methodological Approach Key Findings Practical Implications Summary Resources Overview In clinical research, sample sizes are often small due to cost, recruitment, or ethical constraints. Traditional (frequentist) structural equation models (SEM) and…
Oct 03,2025
Case Study: Customer Relationship Management in a Media Company – User Behavior Analysis
Challenge A media company faced the challenge of gaining a deeper understanding of user behavior on its online portals. The goal was to build a reliable data foundation for strategic decisions and to optimize user…
Oct 03,2025
Case Study: Customer Relationship Management in Medical Technology – Building Data-Driven Marketing
Challenge A medical technology company needed to enhance its customer relationship management by leveraging data to drive marketing effectiveness. The existing approaches to user engagement were not sufficiently targeted, resulting in missed opportunities for customer…
Oct 01,2025
Case Study: Optimizing Electricity Procurement Costs
Challenge Rising energy prices and complex load profiles made it increasingly difficult for companies to keep procurement costs under control. The goal was to develop a data-driven solution to identify savings potential and optimize electricity…
Aug 27,2025
Case Study: Predictive Maintenance for Medical Devices
1. Problem Description and Objective We aim to anticipate equipment failure in 75 medical devices using multivariate longitudinal telemetry data. We develop and evaluate a predictive model, document our approach, and implement an early warning…
Aug 27,2025
Introduction to Statistical Decision Theory – Edition 1: From Traditional Statistical Methods to Decision Theory
1. Basic Idea Statistics is the science of learning from data. It has two main purposes: To describe what has been observed. To make inferences about the wider world beyond the observed data. Often, we…
Aug 27,2025
Introduction to Statistical Decision Theory – Edition 2: Foundations of Estimation and Hypothesis Testing
1. Point Estimation Example Estimating Variance in the Normal Model Let \(X_1, \ldots, X_n \sim N(\mu, \sigma^2)\). We are interested in estimating \(\theta = \sigma^2\). Well-known estimators: Sample variance (unbiased): $$\hat{\sigma}^2 = \frac{1}{n-1} \sum_{i=1}^n (X_i…
Aug 26,2025
Introduction to Statistical Decision Theory – Edition 3: Ethical and Strategic Decisions Under Uncertainty
This third edition presents a real-world-inspired decision-theoretic scenario rooted in the classical formulation of I. J. Good (1952), where human incentives, ethical dilemmas, and corporate strategy intersect. A new invention is presented to a company’s…
Aug 26,2025
Making Bayesian Statistics Accessible to Everyone – Edition 2: Motivating Examples to Deepen Intuition
Introduction In the first edition of this series, we introduced the fundamental distinction between the frequentist and Bayesian paradigms, using the estimation of a population mean as a guiding example. We explored how the Bayesian…
Aug 26,2025
Introduction to Statistical Decision Theory- Edition 4: Cold Weather, Warm Decisions
A Practical Decision Under Uncertainty: Heating or Buying a Pullover? In this fourth edition, we analyze a relatable and simple decision problem using the framework of statistical decision theory. The goal is to determine the…
