Modeling the Transmission of Intelligence within a Bayesian Framework

Nolle, Gerlijn (2018) Modeling the Transmission of Intelligence within a Bayesian Framework. Bachelor's Thesis, Mathematics.

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Abstract

Psychological studies have shown that intelligence clusters within families. This similarity between parents and their children is the result of a mixture of genetic and environmental factors. In this paper it is described how we can infer a model of transmission of intelligence within a Bayesian framework. The Metropolis Hastings MCMC algorithm has been applied for model inference. A data set containing scores of parents and twins who completed the RPM has been used. The RPM is a standardized test for quantifying the intelligence of an individual. After an description of how we can use the Metropolis Hastings algorithm to generate samples from the marginal posterior distributions of the parameters of our model, several plots of these samples are shown. The trace plots show that the Markov chain has converged within 1.000.000 iterations for all model parameters. The density plots show that the marginal posterior distributions of the parameters have the shape of a normal distribution. Finally, two methods have been provided that can determine if the transmission of intelligence of an individual is dominated by genetic factors or environmental factors. The first approach showed that for approximately one half of the twins the transmission of intelligence is dominated by genetic factors. The transmission of intelligence of the other part of the twins is dominated by environmental factors. Further research is necessary to determine the number of twins for which the transmission of intelligence is dominated by genetic factors and the number of twins for which the transmission of intelligence is dominated by environmental factors in the second approach.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Grzegorczyk, M.A. and Krijnen, W.P.
Degree programme:	Mathematics
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	13 Jul 2018
Last Modified:	27 Jul 2018 12:53
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/17865

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