Submitted by johantykesson on July 4, 2019 - 15:33
During the fall 2019 professor Richard Davis, Columbia University, will give a course on Topics in Time Series Analysis: Old to New.
The course is aimed at advanced masters students and PhD students from Chalmers and Gothenburg University, and also welcomes students from other Scandinavian universities. The first meeting will be
Monday, September 23, 13:15-15:00, room MVH 12 in the Mathematics Building, Chalmers tvärgata 3
The Department of Statistics at Uppsala University plans to give an introductory PhD course in philosophy of science in May-June and August-September 2019, see attached file.
Submitted by pergosta on December 18, 2018 - 10:23
The aim is to provide some theory concerning asymptotic methods of statistics and probability with applications to inference problems. Knowledge of measure theory is not needed. Basic convergence concepts and results (with proofs) such as the Lindeberg-Lévy central limit theorem should have been covered in previous courses.
To sign up for the course send an e-mail to per.gosta.andersson@stat.su.se
Submitted by jolanta.pielasz... on March 16, 2018 - 18:12
Course Code:3FNA023 ECTS credits: 5 Level Doctoral course Course contents
The course will cover the following topics:
- an introduction to wavelet analysis
- the Discrete Wavelet Transform (DWT)
- the Maximal Overlap DWT (MODWT)
- the wavelet variance, covariance, correlation and cross correlation
Submitted by johantykesson on November 6, 2017 - 22:59
Approximate Bayesian Computation (ABC) is an increasingly popular inference paradigm in applications where traditional (Bayesian or frequentist) inference is difficult because the likelihood function is e.g. computationally costly to evaluate or unavailable in closed, tractab- le form. In the course, we will start by briefly studying traditional Bayesian inference and different kinds of simulation-based inference methods in general.
The course covers some advanced models in machine learning.
The models are analyzed mainly from a Bayesian perspective.
The course is also a master's course on the LiU programme 'Statistics and Machine Learning'.
The course is organized into four topics:
- Graphical Models
- Hidden Markov Models
- Gaussian Process Regression and Classification
- State-space models
Each topic includes lectures, a computer lab and a follow-up seminar.
Submitted by Lars Forsberg on September 8, 2016 - 10:55
The course offers an introduction to financial econometrics for second-cycle studies. It
covers the main parts of the spectrum of quantitative financial economics, discusses
important results in the empirical finance literature, and provides a comprehensive
knowledge to do empirical work in financial practice.
A student who has taken the course should:
- have a solid knowledge about basic themes in financial econometrics;
- know and be able to use concepts and notation that is frequently used in financial
econometrics;
