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The course focuses on a number of key post-Wall German novels, especially novels from the 1990s which directly or indirectly highlight historical events after the fall of the Wall and the German reunification. The novels are studied in…

The course focuses on a number of key post-war German novels, especially novels from the 1950s and the literary situation related to the influential Gruppe 47. The novels are studied in detail against the background of the political and…

The course is divided into two halves, the first covers probability theory and the second covers statistics. The course covers: Sample space, Events, Basic set theory, Axioms of probability. Conditional probability, Independent events…

Basic probability theory, random variables in one and several dimensions, multivariate Gaussian distribution, convergence of random variables and distributions, conditional distributions. Moment generating functions and characteristic…

The course treats: Markov chains: model graphs, Markov property, transition probabilities, persistent and transient states, positive and null persistent states, communication, existence and uniqueness of stationary distribution, and…

The course treats stochastic processes in discrete and continuous time. The main elements are: Models for stochastic dependence.Concepts of description of stationary stochastic processes in the time domain: expectation, covariance, and…

The student chooses in consultation with supervisors and examiner an independent examination assignment comprising 15 credits. The assignment can either be linked to current scientific projects at the Division of Mathematical Statistics or…

The content and execution of the degree project are planned in consultation with a supervisor. The degree project consists of an independent smaller research assignment that can either relate to current projects at the department or to…

The course gives an overview of simulation based methods of statistical analysis. Markov chain methods for complex problems, e.g. Gibbs sampling and the Metropolis-Hastings algorithm. Bayesian modelling and inference. The re-sampling…

The course is given during the autumn semester at Lunds university and at the Technical University of Denmark in Lyngby. The graduate course in Advanced Time Series Analysis has its target audience amongst students with technical or…

Extreme value theory concerns mathematical modelling of extreme events. Recent developments have introduced very flexible and theoretically well motivated semi-parametric models for extreme values which now are at the stage where they can…

Time series analysis concerns the mathematical modelling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the…

The course deals with model building and estimation in non-linear dynamic stochastic models for financial systems. The models can have continuous or discrete time and the model building concerns determining the model structure as well as…

Least squares and maximum-likelihood-method; odds ratios; Multiple and linear regression; Matrix formulation; Methods for model validation, residuals, outliers, influential observations, multi co-linearity, change of variables; Choice of…

Bayesian methods for stochastic modelling, classification and reconstruction. Random fields, Gaussian random fields, Kriging, Markov fields, Gaussian Markov random fields, non-Gaussian observations. Covariance functions, multivariate…

Basic definitions. Continued study of AR (auto regressive), MA (moving average) and ARMA-processes. Line spectrum and parametric estimating methods. Sub space based techniques. Non-parametric spectral estimators, data-adaptive techniques…

The course consists of two related parts. In the first part we will look at option theory in discrete time. The purpose is to quickly introduce fundamental concepts of financial markets such as free of arbitrage and completeness as well as…

The course treats: Functions of several variables: basic topology in R3, continuity, Differential calculus of functions of several variables: partial derivatives,differentiability, the chain rule, gradient and directional derivatives…