LEARNING OUTCOMES
The student understands the Bayesian basis of estimation in linear and nonlinear dynamic systems. The student understands the principles behind approximate filters and smoothers, and is able to use them in practice. The student knows how to estimate parameters online and offline in nonlinear systems.
Credits: 5
Schedule: 12.01.2022 - 13.04.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Ville Kyrki, Simo Särkkä
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:
Statistical modeling and estimation in nonlinear and nonGaussian systems. Bayesian filtering and smoothing theory. Extended Kalman filtering and smoothing, sigma point and unscented filtering and smoothing, sequential Monte Carlo particle filtering and smoothing. Adaptive nonlinear filtering; ML, MAP, MCMC, and EM estimation of system parameters. Example applications from navigation, remote surveillance, and time series analysis.
Assessment Methods and Criteria
valid for whole curriculum period:
Final exam, home exercises, and project work.
Workload
valid for whole curriculum period:
Contact teaching 26 h (lectures and exercise sessions), independent studies and project work
110 h, examination 3 h
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IV