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HBO/WO

MBO

2023

Lecture Notes chapter 3: Uncertainty Analysis

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 3 of the lecture notes introduces Uncertainty Analysis using Python. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

1 februari 2021

Uncertainty and decision-making

These two presentations deal with decision making and understanding the implications of deep uncertainty from different perspectives. Student will learn to understand that uncertainty is present in all parts of decision-making and modeling and that this has (huge) implications for planning & policy-making and the (unintended) impact of such policies. Students will also learn understanding uncertainty from different perspectives: • Interconnectedness • Uncertain projections • Lack of (reliable) data • Cascading consequences • Risks • Options

Presentatie

Tina Comes

13 april 2017

Uncertainty in Measurements

Modules Measurement Uncertainty and Soil Moisture

Website

Auteur onbekend

21 juli 2026

Introduction to Stochastic Hydrology

Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.

Document

Marc F.P. Bierkens

4 december 2023

In a Nutshell | Why We Risk It with Kim Fairley

In today's episode, Jara talks with Kim Fairley about taking risks and living with uncertainty. How risky is playing the lottery? Why would anyone go free solo climbing? And most importantly, do we need to risk it to get the biscuit? The fifth episode of In a Nutshell is all about risking it. From gambling to putting one’s life on the line – this episode will take you onto the uncertain paths of life. And this week’s guest Kim Fairley will be the navigator on this exploration of risk. Kim Fairley is an assistant professor of Economics with specific expertise in behavioural economics at the Nijmegen School of Management. Unlike most people, Kim does not avoid risks but instead investigates them to share her useful findings with her audience. Do you want to know more about risk, uncertainty, and insecurity? Follow Kim’s tip and listen to the podcasts Freakonomics Radio and ParentData with Emily Oster.

Audio

Antonia Leise, Jara Majerus, Kim Fairley

2 november 2020

Course on Robust Policies for Operations Management Problems: Module 1 - Sources of Uncertainty, Probability Theory and its Limitations

This resource concerns module 1 of the course 'Robust policies for operations management problems'. The resources consist of 2 PPT's and the related web -lectures. Learning objectives Sources of uncertainty Limitation of working with average values Limitation of working with chance constraints Why we should use Robust Optimization The material is designed for MSc students in the field of Operations Management. To understand the material, basic knowledge on linear algebra as well as linear optimization is required. Note that the powerpoint version may differ slightly from the PDF version. Please consult the PDF for the original content.

Presentatie

Ahmadreza Marandi

2 november 2020

Course on Robust Policies for Operations Management Problems: Module 2 - Static Robust Optimization

This resource concerns module 2 (Static robust optimization) of the course on 'Robust policies for operations management problems'. It includes web lectures and slides concerning the following topicsand learning objectives: 2.A: Selecting a robust counterpart o How to formulate Robust Optimization mathematically o How to reformulate it using linear programming duality 2.B: Vertex numeration and the deterministic reformulation o Drawback of using the reformulation obtained by duality o How to use vertex enumeration as an alternative technique 2.C: Robust facility location o How to construct the RC using a deterministic model o How to reformulate RC by analyzing the model o What can we get from solving RC 2.D: Dealing with a set of uncertainties o What the effect of the uncertainty set is in the solution o How we can select the parameters of an uncertainty set The material is designed for MSc students in the field of Operations Management. To understand the material, basic knowledge on linear algebra as well as linear optimization is required. There is a typo in the video of 2A. Robust Counterpart. From 38:55 onward, when we use the duality to reformulate the problem, there is a typo that is carried through the rest of the answer. The correct dual is provided in the lecture note page 8 and the final reformulation is on page 9, equation (2.14).

Presentatie

Ahmadreza Marandi

2023

Maker Lab Assignment: Programming and Data Analysis: Python Exercises

Before the Maker Lab students begin the open inquiry phases, they first learn and practice some important skills, supported by small assignments. In this assignment they are tasked with using a worksheet in Jupyter Notebook and the Python programming language for data analysis and visualization and uncertainty analysis of a provided data set. The analyzed data set is generated quickly via an online survey completed by all students. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Contents: student worksheet in Jupyter Notebook: Programming-and-DataAnalysis_Python-Exercises.ipynb (and .pdf) instructor link to Google Forms survey for data collection: https://docs.google.com/forms/d/e/1FAIpQLSfWsdadhq-v3ubt1p3EIkzxAVyAyQ-1hBYst_RodV7PEp9E7A/viewform?usp=sharing example of data file: example_Data_Collection_For_Python_Exercises.csv

Document

Forrest Bradbury

9 november 2020

Decisions for seasons

“Decisions for the Seasons” is an intensely interactive game designed to support learning and dialogue about key aspects of long-term investments under uncertainty, with a special focus on financial preparedness needs Learning objectives Planning for extremes, Experiencing climate change impacts, Cooperation to better manage risk. Dealing with uncertainty (investments) The play is for 6-100 people and takes 30-60 minutes.

Document

Red Cross Red Crescent Climate Centre

2023

Maker Lab Lecture Slides and Lecture Announcements

This collection of the Maker Lab's Lecture Slides and Lecture Announcements (alongside the Lecture Notes and several Assignments files) detail the learning material and activities that prepare students for the open inquiry phases. Specifically, students learn about experimental design and uncertainty analysis, and learn and practice some important programming skills for controlling sensors and processing and analyzing data. For context of how these lectures fit into the course activities, please see the example course schedule (published as a sub-page of the main "Maker Lab: full course materials" page). The Lecture Slides are .PDF files of the Powerpoint slides, while the .TXT documents are pre- or post-lecture announcements posted to the course's digital learning environment. The course runs over 15 teaching weeks, with two 90 minute class sessions per week ("a" and "b"), which explains the naming system of these files, with names that begin with the week number and session letter. Note that many class sessions (especially after the open inquiry phases begin in session 5b) have no lectures or other materials because they are devoted to students' team presentations or individual meetings between teams and instructor. Additionally, lectures 1b and 2a include exercises and discussions using Arduino code files, with the file extensions .INO, which are also included here. NOTE: These uploads are not yet working. Please reach out if you're interested in the files. NOTE: Instructors interested in having the original powerpoint files of the Lecture Slides are encouraged to reach out to me via forrestbradbury ("AT") gmail.com .

Document

Forrest Bradbury