Found 11 relevant results in 1.41s where lecturer="Hans Rudolf Künsch"
In this course we study the basics of theoretical statistics. The course includes methods for designing estimators, confidenceintervals and tests, and various ways to evaluate the accuracy ofestimators, confidence intervals and tests. We consider optimality criteria such as admissibility and minimaxity, as well asBayesian criteria. We will also present the asymptotic point of view.
Linear Algebra and Statistics
Mathematik IIB: Lineare Algebra und Statistik
Systems of linear equations; matrix algebra, determinants; vector spaces, norms and scalar products;linear maps, basis transformations; eigenvalues and eigenvectors.Least squares fitting and regression models; random variables, statistical properties of least-squares estimators;tests, confidence and prediction intervals in regression models; residual analysis.
Mathematical Foundations II: Linear Algebra and Statistics
Grundlagen der Mathematik II (Lineare Algebra und Statistik)
Linear Algebra:linear systems, vector calculus, matrix calculus, linear maps, orthogonal maps, trace & determinant, eigenvalues & eigenvectors, vector spacesstochastics:combinatorics, probability, probability densities, statistics
Probability and Statistics
Wahrscheinlichkeit und Statistik
- Diskrete Wahrscheinlichkeitsräume- Stetige Modelle- Grenzwertsätze- Einführung in die Statistik
Seminar on Statistics: Bayesian Statistics
Seminar über Statistik: Bayesian Statistics
The seminar discusses the Bayesian paradigm where also the unknown parameters are considered as random variables. Topics include prior, posterior and likelihood, differences to frequentist statistics, empirical Bayes procedures, nonparametric Bayesian methods, asymptotic properties of the posterior, model selection and computational methods.
Seminar on Statistics: Inverse Problems in Statistics
Seminar über Statistik: Inverse Problems in Statistics
Examples of inverse problems are Wicksell's problem,censoring, deconvolution and the indirect regression model.We study minimax lower bounds, plug-in and(nonparametric) maximum likelihood estimators, andalgorithms for computing the maximum likelihood estimator,such as the EM algorithm. Also the asymptotic propertiesof the estimators are examined.
Spatial Statistics and Image Analysis
Räumliche Statistik und Bildanalyse
Gaussian random field models, parameter estimation and linear interpolation (kriging).Markov random field models on a lattice, Gibbs representation, applications to imagedenoising and deblurring, Markov chain Monte Carlo and simulated annealing asbasic computational tools. Models for point pattern and concepts of stochastic geometry.
Statistics I
Statistik I
Introduction to basic methods and fundamental concepts of statistics and probability theory for non-mathematicians. The concepts are presented on the basis of some descriptive examples.
This course provides an introduction to statistical Monte Carlo methods. This includes applications of simulations in various fields (Bayesian statistics, statistical mechanics, operations research, financial mathematics), algorithms for the generation of random variables (accept-reject, importance sampling), estimating the precision, variance reduction, introduction to Markov chain Monte Carlo.