Papers by Micha den Heijer
A Second-Order Adaptive Network Model for Political Opinion Dynamics
IFIP advances in information and communication technology, 2024
Thesis Chapters by Micha den Heijer
This thesis examines the parallelization techniques that can be used to speed up econometric comp... more This thesis examines the parallelization techniques that can be used to speed up econometric computations. Specifically, Monte Carlo simulations implemented using the R programming language. Parallelized Monte Carlo simulations can use multiple processing elements simultaneously, thereby providing answers to computational problems quicker. Three Monte Carlo experiments were examined; a Monte Carlo estimation of the performance of a Bootstrap confidence interval, a Monte Carlo estimation of a Maximum Likelihood Estimation, and the Metropolis-Hastings Independence Chain algorithm. Each experiment was implemented using multiple parallelization techniques, which were tested using multiple parallelization distribution methods. Analysis of the parallel programs shows that parallel Monte Carlo simulations with coarsegrained subproblems, i.e., relatively large subproblems, perform better compared to programs with fine-grained subproblems. The impact of the granularity of the subproblems also appears to increase as the delay between processing elements increases.
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Papers by Micha den Heijer
Thesis Chapters by Micha den Heijer