Bounding the number of contributors to mixed DNA stains

We derive a simple inequality for the probability of observing a given DNA profile when assuming a fixed number of unknown persons have contributed to the mixed stain. We then show how this inequality can be used to obtain an upper bound for the number of unknown contributors needed to be considered.     

Bounding the number of contributors to mixed DNA stains

We derive a simple inequality for the probability of observing a given DNA profile when assuming a fixed number of unknown persons have contributed to the mixed stain. We then show how this inequality can be used to obtain an upper bound for the number of unknown contributors needed to be considered.     

Probabilistic expert systems for handling artifacts in complex DNA mixtures

This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example.     

Identification and separation of DNA mixtures using peak area information

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture. It can also be used to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed.