Volker J Schmid

Professor for Bayesian Imaging and Spatial Statistics

Department of Statistics, Ludwig-Maximilians-Universität München

Volker J Schmid is a professor of statistics with a focus on Bayesian statistics for image and spatial analysis. His research interest includes efficiency of Bayesian computational methods. Applications of these methods include medical imaging and microscopic imaging in biology, as well as traditional applications in spatial statistics like disease mapping.

He is leader of the Bayesian imaging and spatial statistics group at the Department of Statistics at LMU Munich. He is involved in several initiatives which link statistics and data science, including the Munich Center of Machine Learning.


  • Bayesian Statistics
  • Spatial Statistics
  • Imaging


  • Dr. rer. nat. (PhD) innStatistics, 2004

    Ludwig-Maximilians-Universität München

  • Diploma in Statistics, 2000

    Ludwig-Maximilians-Universität München

  • Abitur, 1993

    Joseph-von-Fraunhofer-Gymnasium Cham


Bayesian Age-Period-Cohort-Modelling and Prediction

BAMP is a software package to analyze incidence or mortality data on the Lexis diagram, using a Bayesian version of an …


R tools for working with images in 3D and 4D, mostly for biology/microscopy.


Nucleome Imaging Toolbox is an R package for quantitative analyses of the 3D nuclear landscape recorded with super-resolved …

Software for Magnetic Resonance Imaging

Software for Analysing Data from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI), Cardiac Magnetic Resonance (CMR) …


Regular lectures

Spatial Statistics (Master level)

  • Spatial processes on lattices
  • Geostatistics
  • Point processes
  • Geoadditive regression
  • Spatio-temporal processes

Bayesian Modelling (Master level)

  • Bayesian Hierarchical Modeling
  • Semi-parametric Models
  • Mixed Distribution Models
  • Dirichlet Process Mixtures
  • MCMC and Approximate Inference
  • Bayes Software

Introduction to Bayesian Statistics (German, Bachelor level)


  • Fundamentals of Bayesian Statistics
  • Bayesian Software
  • Introduction to MCMC
  • Bayesian Hierarchical Modeling

Wahrscheinlichkeitstheorie (Bachelor Statistik und Data Science, Compulsary 2nd Semester)

  • Introduction to measure theory
  • Discrete and continuous distributions
  • Moments
  • Covergence of distributions and important theorems
  • Multidimensional distributions