2012/05/21 by Jessica W. Leigh, Jessica Leigh, Leigh, Jessica W. +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · #Algorithm #Applied mathematics #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian probability #Computation (stat.CO) #Computer science #Dimension (graph theory) #Econometrics #Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Genetic Associations and Epidemiology #Grid #Machine learning #Markov Chains and Monte Carlo Methods #Markov chain #Markov chain Monte Carlo #Mathematical optimization #Mathematics #Monte Carlo method #Parallels #Parameter space #Physics #Populations and Evolution (q-bio.PE) #Statistical physics #Statistics #q-bio.PE #stat.CO
paper · pdf · doi:10.48550/arxiv.1205.4503
29 pages incl. supplementary information. 4 figures, 2 supp. figures, 2 supp. tables
arxiv created 2012/05/21 · openalex publication_date 2012/05/21 · arxiv updated 2012/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Simulations often involve the use of model parameters which are unknown or uncertain. For this reason, simulation experiments are often repeated for multiple combinations of parameter values, often iterating through parameter values lying on a fixed grid. However, the use of a discrete grid places limits on the dimension of the parameter space and creates the potential to miss important parameter combinations which fall in the gaps between grid points. Here we draw parallels with strategies for numerical integration and describe a Markov chain Monte-Carlo strategy for exploring parameter values. We illustrate the approach using examples from phylogenetics, archaeology, and epidemiology.