Inference Theory
This course gives you an in-depth understanding of basic statistical principles, such as the principles of sufficiency, ancilliarity, invariance, and conditionality. Bayesian, likelihood-basedand Neyman-Pearson inference are applied and exemplified through point-estimation, interval estimation, and hypothesis testing.
You will learn about important theorems in inference theory and convergence-properties of estimators. You will also learn how to derive important point estimators, interval estimators, and test statistics in some selected applications.
The course provides a solid base for research studies in statistics.
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Course structure
The course is given at day time, full time.
The course forms a part of the Master's Program in Statistics, but it can also be studied as a freestanding course.
Teaching format
The teaching forms consist of lectures and exercises.
Language: English.
Course Information
More information for registered students will be found in Athena.
Assessment
Examination will be in the form of written and oral examination.
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course. -
Course reports
