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| Semester | spring semester 2019 |
| Course frequency | Every spring sem. |
| Lecturers | Penelope Vounatsou (penelope.vounatsou@unibas.ch, Assessor) |
| Content | Introduction to probability theory; Bayesian inference and computation; Regression models for continuous, binary, polytomous and count independent and correlated data; Models for spatio-temporal data; Meta-analysis of clinical trials; Modelling diagnostic error. Statistical inference will be taught using the frequentist and Bayesian approaches |
| Learning objectives | To understand the differences between Bayesian and maximum likelihood inferences, to formulate models within the Bayesian paradigm for different types of independent and correlated outcome data, to model different sources of variation, to estimate the model parameters using existing software and interpret the results. |
| Comments | Methods: Lectures, exercises, computer practicals and project work |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
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No dates available. Please contact the lecturer.
| Modules |
Courses: Master Infection Biology (Master's Studies: Infection Biology) Courses: Master's Studies Epidemiology (Master's Studies: Epidemiology (Start of studies before 01.08.2017)) Modul: Fields: Public Health and Social Life (Master's degree program: African Studies) Module: Advances in Epidemiology, Statistics and Global & Public Health (Master's Studies: Epidemiology) Module: Applications of Machine Intelligence (Master's Studies: Computer Science) |
| Assessment format | continuous assessment |
| Assessment details | Assignments, written exam |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Schweizerisches Tropen- und Public Health-Institut |