Search results for “probabilistic”

Search results (20)

HBO/WO

MBO

June 6, 2025

How To Write A Quantitative Research Paper

So, you need to write a ‘quantitative’ research paper, and you want to know: what is the correct way of writing such a paper? The answer is, unfortunately: it depends … For one, there is no universally accepted, ‘correct’ way of writing any paper in the social sciences in general. Moreover, even just within the so-called ‘quantitative’ research tradition specifically, there exist different viewpoints on what makes for a good research paper. To muddy things up even further, how to write a paper will also depend on the specific research question(s) that it addresses. Hence, even the same scientist might opt for varying approaches in different papers. Having said that, it is possible to identify some common rules and practices that are often followed, as well as a number of practices that should generally be avoided. Based on these common rules and practices, this document is meant to help you on your way with writing a ‘quantitative’ research paper by offering a number of tips, tricks and rules-of-thumb. None of these rules and practices are set in stone, and you (or your teacher/supervisor) may have excellent arguments to deviate from them. However, in general, following them will greatly increase the quality of your paper.

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Alex Lehr, Roderick Sluiter

June 6, 2025

Multilevel modeling for social scientists

The course builds on the knowledge and skills acquired in the previous methods courses. The course will introduce students to multivariate analyses of nested data using multilevel modeling techniques. The course focuses on the theory and application of fixed and, in particular, random effects models. Using data from the European Social Survey, the students will learn how to estimate and interpret such as models using Stata.

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Alex Lehr

September 1, 2024

Critical AI Literacy e-learning course

Update May 12, 2025: The Critical AI module has been faithfully remade in Wikiwijs to be openly available to those who cannot access the University of Groningen's Brightspace. The form is identical to the Brightspace version, and as such can be used as an example for those looking to remake the module in their own online learning environment/LMS. The Wikiwijs module can be found by following the link. This is a collection of relevant files for the first version of the University of Groningen's Critical Ai Literacy e-learning course. This e-learning course was implemented in the university's LMS Brightspace, and is aimed at both students and teachers who want learn more about the fundamentals of Text-based Generative AI models such as ChatGPT. The course tackles five topics: What is AI? How do you Generative AI models work? What are the limitations of Generative AI models? What ethical implications are associated with Generative AI models? What are some applications and considerations for using Generative AI models. Each section tries to explain its content in relatively simple terms, so that anyone regardless of educational background can gain a working understanding of Generative AI Models. Consequently, some of the information may be heavily simplified or does not fully extent to the finer details of AI technologies. As a simple introduction to Generative AI models, this e-learning can be followed on its own, or it can be implemented in other courses focusing on topics such as Academic Skills or Information Literacy. There are three kinds of files available: Word documents that have all the texts and images for each section (5 sections, each tackling one of the topics, an introduction section, and a references and future readings section), video files for introductory videos as well as supplemental animations (the word documents refer to these videos), and powerpoints that are the basis for the animations. For reuse, we recommend using these files to build the e-learning in your institution's own e-learning platform or LMS.

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Lilian Tabois, Alicia Streppel, Rob Nijenkamp, Martijn Blikmans-Middel, Yvonne de Jong

June 12, 2024

Probabalistic Analysis of Classical Oscillator

This virtual lab serves as a bridge from classical mechanics to the inherently probabilistic nature of quantum theory. It allows students to analyze a classical system, here a block oscillating on a spring, in a probabilistic way. This is achieved through a photographer that takes snapshots (photos) of the oscillating block at random times. The position of the block is binned, building up a histogram of snapshot numbers versus position bins. Students can change the bin size, and convert the snapshot histogram into a probability density histogram. They can also display the theoretical probability density histogram, as well as the theoretical probability density curve as the bin size tends to zero. Students can see that the more measurements they take, the smaller on average the deviations of their data from the theoretical values. A second tab allows students to solve a number of challenges. The challenges probe the primary aims outlined below. Primary aims: Students can describe the experimental procedure shown in the lab for analyzing a classical system in a probabilistic way. Students can describe qualitatively how the block’s speed in a given region relates to the number of snapshots in that region. Students understand the difference between a probability and a probability density. They can perform calculations to find probabilities and probability densities from the number of snapshots in a given region.

UniversityWebsite

Antje Kohnle, Mark Paetkau

February 29, 2024

Risk and Reliability for Engineers

This book covers a wide range of topics that involve the use of probability to solve problems in engineering design and research. Although it is relevant for a wide range of disciplines, it draws heavily on the fields of civil engineering, environmental engineering and the geosciences. Specific topics include risk analysis, probabilistic design, reliability-based design (component and system reliability). Future versions of this book will include additional applications of probability theory related to continuous distributions, extreme value analysis and expanded chapters on component and system reliability. A number of practical examples and exercises are included, both analytic and numerical, some of which use an interactive feature that allows Python code to be used in the web browser without any additional software installation.

Open (Access) Text Book

Robert Lanzafame

August 23, 2023

Lecture: Inference and Prediction

Learners understand the basic idea of Bayesian thinking, Learners are familiar with ML and MAP inference with various distributions, Learners understand the algorithmic aspects of ML/MAP inference and prediction, Learners understand the idea of Bayesian model averaging and probabilistic predictions.

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HCAIM Consortium

August 23, 2023

Lecture: Decision Networks

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

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HCAIM Consortium

August 23, 2023

Tutorial: Inference and Prediction

Learners understand the basic idea of Bayesian thinking, Learners are familiar with ML and MAP inference with various distributions, Learners understand the algorithmic aspects of ML/MAP inference and prediction, Learners understand the idea of Bayesian model averaging and probabilistic predictions.

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HCAIM Consortium

August 23, 2023

Lecture: Natural Language Processing

Students understand the basic concepts of Natural Language Processing Students learn NLP use cases Students get familiar with various NLP tools and concepts

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HCAIM Consortium

August 23, 2023

Lecture: Unsupervised Learning

Learn basics of unsupervised learning

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HCAIM Consortium