JK
J.F. Koks
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At the Netherlands Forensic Institute, additional replicate measurements of the same DNA trace, referred to as rework, can be performed to obtain more information from a DNA mixture profile. Rework may increase the evidential value, expressed by the likelihood ratio (LR), but it also costs laboratory time, resources, and DNA sample material. This thesis investigates whether the LR after rework can be predicted from the original DNA mixture profile.
Two main contributions were made. First, a simulation framework was developed to construct predictive distributions for the rework LR. Starting from the deconvolution of the original profile, plausible contributor genotypes are sampled, additional replicate profiles are simulated, and the LR of the combined profile is calculated. Second, a Bayesian MCMC implementation was developed for the EuroForMix/DNAStatistX peak-height model, making it possible to propagate uncertainty in the nuisance parameters when computing LR values.
The framework was evaluated on cleaned two-person NFI research data, focusing on minor contributors. The frequentist plug-in simulation was not sufficiently calibrated: nominal 95% prediction intervals covered only 69.0% of the observed minor true-donor rework LRs. Including Bayesian parameter uncertainty improved the empirical coverage to 81.6% and reduced the mean interval score from 50.5 to 21.6. However, the predicted distributions remained insufficiently calibrated for casework use.
Overall, this thesis shows that predicting rework LRs is possible in principle and that parameter uncertainty is important for such predictions. The current framework should be viewed as a mathematical proof of concept rather than an operational tool. Further work is needed on artefact modelling, computational scaling, full MCMC validation, extension to more complex mixtures, and validation on casework-like data. ...
Two main contributions were made. First, a simulation framework was developed to construct predictive distributions for the rework LR. Starting from the deconvolution of the original profile, plausible contributor genotypes are sampled, additional replicate profiles are simulated, and the LR of the combined profile is calculated. Second, a Bayesian MCMC implementation was developed for the EuroForMix/DNAStatistX peak-height model, making it possible to propagate uncertainty in the nuisance parameters when computing LR values.
The framework was evaluated on cleaned two-person NFI research data, focusing on minor contributors. The frequentist plug-in simulation was not sufficiently calibrated: nominal 95% prediction intervals covered only 69.0% of the observed minor true-donor rework LRs. Including Bayesian parameter uncertainty improved the empirical coverage to 81.6% and reduced the mean interval score from 50.5 to 21.6. However, the predicted distributions remained insufficiently calibrated for casework use.
Overall, this thesis shows that predicting rework LRs is possible in principle and that parameter uncertainty is important for such predictions. The current framework should be viewed as a mathematical proof of concept rather than an operational tool. Further work is needed on artefact modelling, computational scaling, full MCMC validation, extension to more complex mixtures, and validation on casework-like data. ...
At the Netherlands Forensic Institute, additional replicate measurements of the same DNA trace, referred to as rework, can be performed to obtain more information from a DNA mixture profile. Rework may increase the evidential value, expressed by the likelihood ratio (LR), but it also costs laboratory time, resources, and DNA sample material. This thesis investigates whether the LR after rework can be predicted from the original DNA mixture profile.
Two main contributions were made. First, a simulation framework was developed to construct predictive distributions for the rework LR. Starting from the deconvolution of the original profile, plausible contributor genotypes are sampled, additional replicate profiles are simulated, and the LR of the combined profile is calculated. Second, a Bayesian MCMC implementation was developed for the EuroForMix/DNAStatistX peak-height model, making it possible to propagate uncertainty in the nuisance parameters when computing LR values.
The framework was evaluated on cleaned two-person NFI research data, focusing on minor contributors. The frequentist plug-in simulation was not sufficiently calibrated: nominal 95% prediction intervals covered only 69.0% of the observed minor true-donor rework LRs. Including Bayesian parameter uncertainty improved the empirical coverage to 81.6% and reduced the mean interval score from 50.5 to 21.6. However, the predicted distributions remained insufficiently calibrated for casework use.
Overall, this thesis shows that predicting rework LRs is possible in principle and that parameter uncertainty is important for such predictions. The current framework should be viewed as a mathematical proof of concept rather than an operational tool. Further work is needed on artefact modelling, computational scaling, full MCMC validation, extension to more complex mixtures, and validation on casework-like data.
Two main contributions were made. First, a simulation framework was developed to construct predictive distributions for the rework LR. Starting from the deconvolution of the original profile, plausible contributor genotypes are sampled, additional replicate profiles are simulated, and the LR of the combined profile is calculated. Second, a Bayesian MCMC implementation was developed for the EuroForMix/DNAStatistX peak-height model, making it possible to propagate uncertainty in the nuisance parameters when computing LR values.
The framework was evaluated on cleaned two-person NFI research data, focusing on minor contributors. The frequentist plug-in simulation was not sufficiently calibrated: nominal 95% prediction intervals covered only 69.0% of the observed minor true-donor rework LRs. Including Bayesian parameter uncertainty improved the empirical coverage to 81.6% and reduced the mean interval score from 50.5 to 21.6. However, the predicted distributions remained insufficiently calibrated for casework use.
Overall, this thesis shows that predicting rework LRs is possible in principle and that parameter uncertainty is important for such predictions. The current framework should be viewed as a mathematical proof of concept rather than an operational tool. Further work is needed on artefact modelling, computational scaling, full MCMC validation, extension to more complex mixtures, and validation on casework-like data.
This research investigates the impact of gravitational scatterings caused by close encounters between particles in an N-body Kepler system, addressing three main questions: (1) the influence of scatterings on system evolution, (2) the correspondence between simulated and expected average times between scatterings, and (3) the effect of increasing different parameters individually on the average scattering time. Simulations demonstrate an average scattering angle of 15.2 degrees for particles involved in the top 10 percent of scatterings. This would indicate a non-negligible impact of gravitational scatterings, especially for systems with heavier bodies. The results indicate that the simulated average time between scatterings is higher than the expected average, necessitating further research for accurate estimation. Moreover, the time between scatterings decreases over time, before reaching a stationary state after roughly 300 scatterings. On this domain, the correlation coefficient between the scattering time and the scattering counter was found to be -0.08. By varying the test domains for different parameters, a new expression for the expected time between two scatterings is proposed based on simulation data. A clear connection was found between the scattering time and the number of particles, the maximum orbital radius and the maximum inclination angle. The study acknowledges limitations, including the non-stationary initialization state and linear approximations to most computations, suggesting avenues for future improvement. Overall, this research aims to find the role of gravitational scatterings in Kepler systems and underscores the need to consider these interactions, which are now often considered to be negligible.
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This research investigates the impact of gravitational scatterings caused by close encounters between particles in an N-body Kepler system, addressing three main questions: (1) the influence of scatterings on system evolution, (2) the correspondence between simulated and expected average times between scatterings, and (3) the effect of increasing different parameters individually on the average scattering time. Simulations demonstrate an average scattering angle of 15.2 degrees for particles involved in the top 10 percent of scatterings. This would indicate a non-negligible impact of gravitational scatterings, especially for systems with heavier bodies. The results indicate that the simulated average time between scatterings is higher than the expected average, necessitating further research for accurate estimation. Moreover, the time between scatterings decreases over time, before reaching a stationary state after roughly 300 scatterings. On this domain, the correlation coefficient between the scattering time and the scattering counter was found to be -0.08. By varying the test domains for different parameters, a new expression for the expected time between two scatterings is proposed based on simulation data. A clear connection was found between the scattering time and the number of particles, the maximum orbital radius and the maximum inclination angle. The study acknowledges limitations, including the non-stationary initialization state and linear approximations to most computations, suggesting avenues for future improvement. Overall, this research aims to find the role of gravitational scatterings in Kepler systems and underscores the need to consider these interactions, which are now often considered to be negligible.