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Course Syllabus.pdf

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🔗 Source: math.kit.edu
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Summary

Theory and Practice II" covers the probabilistic and statistical foundations of forecasting, with a focus on maximizing the sharpness of predictive distributions while ensuring calibration. The course will explore proper scoring rules, information theory, and convex analysis, with concepts illustrated through data examples and case studies. Prerequisites include a firm understanding of probability theory. The course will be held partly on-site and partly online via Zoom, with oral exams covering both Part I and Part II at the end of the summer semester. Statistical software, particularly R, will be used for problem sets, and key technical references include papers by Gneiting and others.

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Description

Theory and Practice II" covers the probabilistic and statistical foundations of forecasting, with a focus on maximizing the sharpness of predictive...

Technical Information

  • File Format: PDF
  • File Size: 27 KB
  • Pages: 2
  • Language: EN
  • Total Downloads: 734
  • Last Updated: 19 hours ago

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