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DTSTART;VALUE=DATE:20251029
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SUMMARY:Statistical methods for SCIENCE (SmS)
DESCRIPTION:Content \nToolbox course on statistical methodology with focus on choice of statistical models\, practical implementation using statistical software\, and presentation and interpretation of results. For the practical implementation\, we use the state-of-the approach for data analysis in R including data wrangling and visualization via the tidyverse package. The course covers the most common statistical models used in the empirical sciences. Specifically\, the following topics are taught: data types\, data visualization\, table-of-counts\, categorical regression\, linear and multilinear regression\, analysis of variance\, random effects\, hypothesis testing and statistical power\, correction for multiple testing\, estimated marginal means and confidence intervals\, and design of experiments. \nFormal requirements \nThere are no formal requirements. However\, recommended prerequisite is some introductory statistics course during the participant’s bachelor or master studies\, or the PhD school Fundamentals II course. \nLearning outcome \nThe students are introduced to statistical models commonly used in the empirical sciences for univariate end-points. The statistical methodology is discussed with emphasis on how models are used and interpreted\, and the students are trained to do the statistical analyses using the statistical software R. \nAfter course completion\, the students should be able to: \nKnowledge:\n• Understand elements of frequentist statistics including estimation\, confidence intervals\, hypothesis tests\, model validation.\n• Understand data types and organization in tidy data.\n• Understand assumptions for statistical analyses.\n• Understand concepts of fixed and random effects.\n• Understand solutions to the multiple testing problem. \nSkills:\n• Identify the data types in a particular dataset\, and choose an adequate statistical model.\n• Make high quality visualizations of data.\n• Report results via the estimated-marginal-means technology.\n• Use R to perform the statistical analysis via the RStudio interface.\n• Use relevant R packages. In particular\, tidyverse\, emmeans\, and lme4. \nCompetences:\n• Formulate scientific questions in terms of statistical hypothesis.\n• Conduct statistical analysis using the discussed models.\n• Interpret the results of a statistical analysis.\n• Critically reflect over the results\, conclusions and limitations of a statistical analysis.\n• Judge when to seek help from a skilled statistician. \nLiterature \n• Chester Ismay\, Albert Y. Kim: “Statistical Inference via Data Science: A ModernDive into R and the Tidyverse”\, CRC Press\, The R Series\, 2019. Book is also available online at www.moderndive.com.\n• Supplementary material on random effects and estimated marginal means.\n• Software R and RStudio is free and may be downloaded from www.r-project.org and www.posit.co. \nTeaching and learning methods \nLectures and exercises including use of computers. In the first half of the course days focus will be on lectures\, and in the second half on individual coursework with exercises. Participants must bring their own laptops with R and RStudio installed. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/statistical-methods-for-science-sms/
LOCATION:Department of Mathematical Sciences                Universitetsparken 5
CATEGORIES:PhD Course
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DTSTART;VALUE=DATE:20240624
DTEND;VALUE=DATE:20240629
DTSTAMP:20260414T214432
CREATED:20240424T085746Z
LAST-MODIFIED:20240424T085746Z
UID:10001141-1719187200-1719619199@ddsa.dk
SUMMARY:2nd Copenhagen School of Stochastic Programming
DESCRIPTION:Content \nThis course provides a rigorous and research-oriented introduction to stochastic programming\, a mathematical framework for decision-making in the presence of uncertainty. In many real-life problems\, important information is unknown to the decision-maker and only distributional information is available. Examples include the scheduling of power generation while demand and renewable production is uncertain\, investments in assets with uncertainty in future returns or production of goods for which demand is stochastic.  \nThe course will start by formalizing such decision problems as mathematical optimization problems and analyzing their fundamental properties. From a computational perspective\, these problems may be extremely challenging. Thus\, a major part of the course will discuss approximations\, either of the underlying distributions or of the optimization problem itself. The former involves so-called scenario generation and stability of the optimization problems. The latter covers various approximation and bounding techniques. The course will proceed with a number of applications in the energy sector\, an area for which stochastic programming has become increasingly important with the adoption of intermittent renewable energy sources. Finally\, a selection of solution methods will be addressed\, including exact decomposition procedures and approximate methods with strong connections to emerging approaches in machine learning.  \nFormel requirements \nPrerequisites:A solid understanding of linear programming theory and some knowledge of probability theory (e.g.\, understanding what probability distributions are for both continuous and discrete random variables).  \nLearning outcome \nThe students will become well acquainted with the theory of stochastic programming and the challenges involved when applying stochastic programming in practice. Particularly\, upon completion of the course\, the students will be able to formulate two-stage and multi-stage stochastic programs\, analyze their properties and discuss their practical implications. They will also learn how to approximate these problems\, generate scenarios and address stability with respect to these\, bound and assess the value of stochastic optimization. Finally\, they will be able to apply and adapt selected traditional and novel solution methods. \nLiterature \nLecture notes and hand-ins provided by the organizers of the course.. \nTeaching and learning methods \nEvery day consists of three hours of lectures and two hours of exercises or project work on the same topic.  \nRemarks \nPlease read more and SIGN UP here: \nhttps://www.math.ku.dk/english/calendar/events/cssp_2/ \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/2nd-copenhagen-school-of-stochastic-programming/
LOCATION:Department of Mathematical Sciences                Universitetsparken 5
CATEGORIES:PhD Course
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