Statistics | Neuro-Mechanistic Modeling

Statistics Lab

Wed, 01 Apr 2026 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Pascal L. Held, Pascal Schumann (assistants)

Course Goals

This course serves as an introduction to the fundamentals of statistics and probability theory and covers most of the core principles of modern data analysis and predictive modeling:

Descriptive Statistics and Visualizations in Python
Probabilities and Discrete Random Variables
Continuous Random Variables and Laws of Large Numbers
Parameter Estimation
Statistical Testing
Computer Simulations and Monte Carlo Methods
Stochastic Processes

Format

The course follows an inverted classroom approach and is divided into three modules:

Descriptive Statistics, Probability & Random Variables
Distributions & Parameter Estimation
Statistical Testing

Each week consists of:

Monday lecture (GHH, Prof. Wolf): a large group session covering the week’s material, including worked examples from the exercise sheet.
Tutorials: small group sessions to work on the previous week’s exercises, discuss solutions, and take mini-tests.
Self-study with ALA: practice exercises with ALA, an AI chatbot tailored to the course content that guides you through problems and answers your questions.
Videos and materials: new learning materials for self-study, released weekly.

To be admitted to the final exam, students complete the exercises and mini-tests in the tutorials and submit several dialogues with ALA.

Organization

All information and materials are available in the CMS.

Spatiotemporal Models and Inference

Tue, 01 Apr 2025 00:00:00 +0000

Team

Dr. Jonas Wahl (lecturer)
Felix Jahn, Pascal Schumann (assistants)

Content

In many scientific fields, data is not just tied to a single location or a single moment in time: it evolves over both space and time. Whether tracking the spread of infectious diseases, predicting climate patterns, modeling traffic flows, or analyzing environmental pollution, understanding how variables change across both dimensions is crucial. This is where spatiotemporal modeling comes in.

Spatiotemporal models aim to capture dependencies and relationships in data that vary over time and across geographical locations. Unlike purely spatial or purely temporal models, these approaches integrate both dimensions, allowing us to uncover trends, make predictions, and quantify uncertainties in dynamic systems. Spatiotemporal models can be of different types:

Deterministic models, based on physical laws or equations (e.g., fluid dynamics for weather modeling).
Statistical models, which use probability distributions to describe uncertainties (e.g., Gaussian processes, Bayesian hierarchical models).
Machine learning models, which learn patterns from large datasets using neural networks, random forests, or deep learning approaches.

The latter two were the main focus of this seminar. Applications of spatiotemporal modeling are found across the sciences, from forecasting the spread of diseases and analyzing temperature changes over decades to optimizing transportation networks and tracking animal migration or deforestation. The seminar covered the theoretical underpinnings of spatiotemporal statistics, Bayesian approaches and hierarchical models, and machine learning techniques for complex spatiotemporal data.

Format

A block seminar for up to 10 participants, with a kick-off in June. Each participant gave a 60-minute presentation. Grading: presentation (50%), participation in the talks of fellow participants and the discussion (25%), and a final report on the topic (25%).

Prerequisites: successful participation in a machine or deep learning course, or a background in statistical modeling, for instance from a course on statistics or stochastic processes.

Topics

Statistics for Spatiotemporal Data, Chapter 2: Statistical preliminaries
Statistics for Spatiotemporal Data, Chapter 3: Fundamentals of Temporal Processes
Statistics for Spatiotemporal Data, Chapter 4: Fundamentals of Spatial Random Processes
Introduction to Gaussian processes (lecture notes here and here, and the book)
Coding tutorial on spatial Gaussian processes (based on this blog post and this one)
Introduction to point processes (lecture notes here, slides here and here)
Coding tutorial on point processes (with the tick package, this simulator, these Jupyter notebooks, or spatial point patterns in R)
Scalable spatiotemporal prediction with Bayesian neural fields, with a coding tutorial on Bayesian neural fields
A Bayesian machine-learning approach for spatiotemporal prediction of COVID-19 cases
Spatial-temporal graph neural networks for groundwater data

Organization

All information and materials are available in the CMS.

Statistics Lab

Mon, 01 Apr 2024 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Emilia Ellsiepen, Joschka Groß, Felix Jahn (assistants)

Course Goals

This course serves as an introduction to the fundamentals of statistics and probability theory and covers most of the core principles of modern data analysis and predictive modeling:

Descriptive Statistics and Visualizations in Python
Probabilities and Discrete Random Variables
Continuous Random Variables and Laws of Large Numbers
Parameter Estimation
Statistical Testing
Computer Simulations and Monte Carlo Methods
Stochastic Processes

Format

A weekly recorded on-site lecture, with a video course and lecture notes as supplementary online resources.
Weekly on-site tutorials in German, English, or bilingual.
Written mini-tests in every second lecture, and graded exercise sheets prepared in groups of 3–4 students.
Exam admission requires at least 50% of the combined score of mini-tests (2/3) and exercise sheets (1/3). Students scoring above 90% receive an exam bonus.
A written on-site exam at the end of the semester.

Organization

All information and materials are available in the CMS.

Statistics Lab

Fri, 01 Apr 2022 00:00:00 +0000

Prof. Dr. Verena Wolf, Paula Kramer, Joschka Groß.

CMS

All information and materials are available in the CMS.

Statistics Lab

Wed, 01 Apr 2020 00:00:00 +0000

Dozentin: Prof. Dr. Verena Wolf

Übungen: Gerrit Großmann, Michael Backenköhler

Organisation: Timo P. Gros

CMS

Alle nötigen Informationen und Materialien finden Sie im CMS.

Organisatorisches

Zeit: Dienstags, 8:30 Uhr
Ort: Hörsaal 2 in Gebäude E1 3

Thema

Das Ziel der Vorlesung ist es, den Studierenden grundlegende Kenntnisse der Wahrscheinlichkeitsrechnung und Statistik zu vermitteln.

Probabilistic Models and Data Analysis

Wed, 10 Apr 2019 00:00:00 +0000

CMS

Please register in the CMS for the lecture. There you can find course materials, news, and more.

Schedule

Monday 10:15 – 11:45, Building E2 1, Room 001
Wednesday 10:15 – 11:45, Building E2 1, Room 007

Objective

The aim of this course is to give students of bioinformatics and computer science a solid background in probability and statistics as well as detailed knowledge about computer simulation of stochastic models, parameter estimation and hypothesis testing. Students will also learn how to use software tools such as Python to analyze data and probabilistic models. This course is a flipped classroom course, which means that the two lecture/tutorial slots will be used for exercises/examples/questions and discussion of the material; participants have to prepare for these slots by either watching videos or reading the lecture notes.

Prerequisites

The course is open to students from bioinformatics and computer science. Mathematical skills as well as basic programming skills are of advantage but not mandatory. For the following topics, we have material (notes, videos) for revision:

Events and their probabilities
Rules of probability
Combinatorics
Conditional probability and independence
Distribution of a random variable
Expectation and variance

Syllabus

Short review of probability and random variables
Computer simulations and stochastic processes
- Simulation of random variables
- Monte Carlo methods
- Markov chains
- Hidden Markov models
Introduction to statistics and statistical inference
- Simple descriptive and graphical statistics
- Parameter estimation: Maximum Likelihood, Bayesian Inference, …
- Confidence intervals
- Hypothesis testing
- Multiple Testing

Text Books

Probability and Statistics for Computer Scientists, Michael Baron, Taylor & Francis, 2013
Simulation Modelling and Analysis, Averill M. Law, McGraw-Hill, 2006
Introduction to the Numerical Solution of Markov Chains, William J. Stewart, Princeton University Press, 1994
Introduction to Probability, C. Grinstead and L. Snell (download)

Simulating the Large-Scale Erosion of Genomic Privacy Over Time

Sun, 01 Jul 2018 00:00:00 +0000

Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium

Wed, 01 Nov 2017 00:00:00 +0000

Probabilistic Models and Data Analysis

Sun, 01 Oct 2017 00:00:00 +0000

Probabilistic Models and Data Analysis

Thu, 01 Oct 2015 00:00:00 +0000