Courses 2011

From SFI

Jump to: navigation, search
  • Bayesian computing with INLA
    NR, November 10, 2011

    Lecturer: Researcher Daniel Simpson, Department of Mathematical Sciences, NTNU

    In these lectures, I will discuss approximate Bayesian inference for a class of models named `latent Gaussian models' (LGM). LGM's are perhaps the most commonly used class of models in statistical applications. It includes, among others, most of (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models.

    The concept of LGM is intended for the modeling stage, but turns out to be extremely useful when doing inference as we can treat models listed above in a unified way and using the *same* algorithm and software tool. Our approach to (approximate) Bayesian inference, is to use integrated nested Laplace approximations (INLA). Using this new tool, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

    In these lectures I will introduce the required background and theory for understanding INLA, including details on Gaussian Markov random fields and fast computations of those using sparse matrix algorithms. I will end these lectures illustrating INLA on a range of examples in R (see www.r-inla.org). Participants are encouraged to bring a laptop with R and INLA installed.


  • Introduction to genetic epidemiology - MF9460
    UiO, November 7-11 2011

    The course is intended to give an understanding of concepts and methods related to genetic epidemiology with focus on both familiy-related linkage analysis and population-based association studies. We will explain general concepts of genetic epidemiology, and demonstrate practical methods and tools needed for different kinds of genetic data. In the hands-on parts of the course we will focus on softwares PLINK (for association) and Merlin (for linkage analysis).

    Course leader: Associate Professor Bettina Kulle Andreassen, Department of Biostatistics, Institute Group of Basic Medical Sciences, University of Oslo.


  • BGC-course: Statistical Learning and Bioinformatics
    University of Copenhagen, September 5 - November 4, 2011

    The main topics of this course are models and methods suitable for analyzing high dimensional data where there are typically many features compared to replications. This is a typical situation met in bioinformatics and exemplified by gene expression data, where we analyze experiments with thousands of parallel measurements and few replications.

    The course focuses on supervised learning where typical approaches to high-dimensional data analysis involve flexible models combined with shrinkage or regularization algorithms, such as ridge or lasso regression perhaps combined with basis expansion techniques such as spline regression and smoothing splines. Also non-generative models such as classification and regression trees are found useful for prediction purposes.

    Lecturer: Associate Professor Niels Richard Hansen, Department of Mathematical Sciences, University of Copenhagen.


  • Introduction to bioequivalence and non-inferiority -MF9450
    UiO, April 4-5 2011

    A new short-course, given by one of the best lecturers in biostatistics, Professor Stephen Senn, University of Glasgow. Senn is one of the key scientists in pharmaco-statistics, with a long experience in both scientific and industrial research.

    The course will give an introduction to bioequivalence and non-inferiority trials, and the analysis of such. As more and more clinical trials are focused on showing equivalence rather than efficacy, this type of studies become more and more common and important. How do you set about showing significant same-ness? This course will introduce the field to the student, paying as much attention to practical matters and philosophical issues as to the mathematics. Some very elementary pharmacokinetic theory will also be covered since it is essential to understanding bioequivalence.

    To follow this course you do not need knowledge in pharmaco-sciences. A basic understanding of clinical trials will be useful, and having covered an introductory course in statistics is a prerequisite.

    Stephen Senn is author of three important books: Cross-over Trials in Clinical Research, Statistical Issues in Drug Development and Dicing with Death. He has contributed to many aspects of statistics that has to do with the pharmaceutical industry and drug development. Before being appointed professor in England and Scotland, Senn had many years experience as a leading statistician in the pharmaceutical industry in Switzerland and UK.

    Course leader: Professor Arnoldo Frigessi, Department of Biostatistics, Institute Group of Basic Medical Sciences, University of Oslo.
Views
Positions
Innovation Areas