Related Experiment Video
Updated: Jul 7, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models
Published on: February 26, 2014
A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics
Rasmus Waagepetersen1, Noelia Ibánez-Escriche, Daniel Sorensen
1Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark. rw@math.aau.dk
Abstract:
In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mutation, Gene Flow, and Genetic Drift
Hardy-Weinberg Principle
Mechanistic Models: Compartment Models in Individual and Population Analysis
Genetic Drift
Multiple Allele Traits