LEARNING OUTCOMES
After the course, the student can explain the central concepts in Bayesian statistics, and name steps of the Bayesian modeling process. The student can recognize usages for common (i.e. those presented during the course) statistical models, and formulate the models in these situations. The student can compare the most popular Bayesian simulation methods, and implement them. The student can use analytic and simulation based methods for learning the parameters of a given model. The student can estimate the fit of a model to data and compare models.
Credits: 5
Schedule: 05.09.2022 - 09.12.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Aki Vehtari
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Bayesian probability theory and bayesian inference. Bayesian models and their analysis. Computational methods, Markov-Chain Monte Carlo.
Assessment Methods and Criteria
valid for whole curriculum period:
Assignments (67%) and a final project work with presentation (33%). Minimum of 50% of points must be obtained from both the assignments and the project work.
Workload
valid for whole curriculum period:
Lectures 10x2h, computer exercises 10x2h, independent studying (text book, programming, home assignment and project reports), project presentation
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn I - II
2023-2024 Autumn I - IIEnrollment :
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.