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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 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: The real numbers: axioms, examples of proofs of basic arithmetical rules. The elementary functions, polynomials, rational functions, the exponential function and the natural logarithm, the trigonometric functions…

The course treats: Elementary logic and set theory Basic properties of the natural numbers and the integers: induction, divisibility, Euclid’s algorithm Basic properties of polynomials: divisibility, Euclid’s algorithm Complex…

The course treats: Functions of several variables, basic topology in R^n. Continuity, theorems about continuous functions. Differentiable functions, gradients and directional derivatives. Taylor's formula. Extreme values…

Variable substitution, inverse and implicit functions, curve and surface integrals, Green's formula, Gauss Divergence Theorem, Stokes Theorem are treated.

The course treats Matrices: matrix operations, matrix inverse, matrix rank Determinants: definition and properties Linear spaces: subspace, span, linear dependence/independence, basis, dimension Euclidean spaces: scalar product,…

The course treats: Properties of the real numbers R: completeness axiom, Cauchy sequences, cardinality of rational and irrational numbers. Topology in Rn: open and closed sets, p-norms, convergence, compactness and the Bolzano…