10 research outputs found
Computational aspects of DNA mixture analysis
Statistical analysis of DNA mixtures is known to pose computational
challenges due to the enormous state space of possible DNA profiles. We propose
a Bayesian network representation for genotypes, allowing computations to be
performed locally involving only a few alleles at each step. In addition, we
describe a general method for computing the expectation of a product of
discrete random variables using auxiliary variables and probability propagation
in a Bayesian network, which in combination with the genotype network allows
efficient computation of the likelihood function and various other quantities
relevant to the inference. Lastly, we introduce a set of diagnostic tools for
assessing the adequacy of the model for describing a particular dataset
Computing Educational Activities Involving People Rather Than Things Appeal More to Women (Recruitment Perspective)
Estimation of Parameters in DNA Mixture Analysis
In Cowell et al. (2007), a Bayesian network for analysis of mixed traces of
DNA was presented using gamma distributions for modelling peak sizes in the
electropherogram. It was demonstrated that the analysis was sensitive to the
choice of a variance factor and hence this should be adapted to any new trace
analysed. In the present paper we discuss how the variance parameter can be
estimated by maximum likelihood to achieve this. The unknown proportions of DNA
from each contributor can similarly be estimated by maximum likelihood jointly
with the variance parameter. Furthermore we discuss how to incorporate prior
knowledge about the parameters in a Bayesian analysis. The proposed estimation
methods are illustrated through a few examples of applications for calculating
evidential value in casework and for mixture deconvolution
Statistical and Computational Methodology for the Analysis of Forensic DNA Mixtures with Artefacts
This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.</p
Statistical and Computational Methodology for the Analysis of Forensic DNA Mixtures with Artefacts
This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors.
We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data.
An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis.
To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the
R package DNAmixtures.This thesis is not currently available on ORA
Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry
In the pharmaceutical industry, the maintenance of production machines must
be audited by the regulator. In this context, the problem of predictive
maintenance is not when to maintain a machine, but what parts to maintain at a
given point in time. The focus shifts from the entire machine to its component
parts and prediction becomes a classification problem. In this paper, we focus
on rolling-elements bearings and we propose a framework for predicting their
degradation stages automatically. Our main contribution is a k-means bearing
lifetime segmentation method based on high-frequency bearing vibration signal
embedded in a latent low-dimensional subspace using an AutoEncoder. Given
high-frequency vibration data, our framework generates a labeled dataset that
is used to train a supervised model for bearing degradation stage detection.
Our experimental results, based on the FEMTO Bearing dataset, show that our
framework is scalable and that it provides reliable and actionable predictions
for a range of different bearings.Comment: Submitted to the KDD Applied Data Science trac