Likelihood Ratio-based Approach Research Articles

Breaking with Trends in Forensic Dating: A Likelihood Ratio-Based Approach for Estimating Time Elapsed Since Bloodstains Deposition

Breaking with Trends in Forensic Dating: A Likelihood Ratio-Based Approach for Estimating Time Elapsed Since Bloodstains Deposition

A likelihood ratio-based approach for improved source attribution in microbiological forensic investigations

A common objective in microbial forensic investigations is to identify the origin of a recovered pathogenic bacterium by DNA sequencing. However, there is currently no consensus about how degrees of belief in such origin hypotheses should be quantified, interpreted, and communicated to wider audiences. To fill this gap, we have developed a concept based on calculating probabilistic evidential values for microbial forensic hypotheses. The likelihood-ratio method underpinning this concept is widely used in other forensic fields, such as human DNA matching, where results are readily interpretable and have been successfully communicated in juridical hearings. The concept was applied to two case scenarios of interest in microbial forensics: (1) identifying source cultures among series of very similar cultures generated by parallel serial passage of the Tier 1 pathogen Francisella tularensis, and (2) finding the production facilities of strains isolated in a real disease outbreak caused by the human pathogen Listeria monocytogenes. Evidence values for the studied hypotheses were computed based on signatures derived from whole genome sequencing data, including deep-sequenced low-frequency variants and structural variants such as duplications and deletions acquired during serial passages. In the F. tularensis case study, we were able to correctly assign fictive evidence samples to the correct culture batches of origin on the basis of structural variant data. By setting up relevant hypotheses and using data on cultivated batch sources to define the reference populations under each hypothesis, evidential values could be calculated. The results show that extremely similar strains can be separated on the basis of amplified mutational patterns identified by high-throughput sequencing. In the L. monocytogenes scenario, analyses of whole genome sequence data conclusively assigned the clinical samples to specific sources of origin, and conclusions were formulated to facilitate communication of the findings. Taken together, these findings demonstrate the potential of using bacterial whole genome sequencing data, including data on both low frequency SNP signatures and structural variants, to calculate evidence values that facilitate interpretation and communication of the results. The concept could be applied in diverse scenarios, including both epidemiological and forensic source tracking of bacterial infectious disease outbreaks.

Data-Efficient Minimax Quickest Change Detection With Composite Post-Change Distribution

The problem of quickest change detection is studied, where there is an additional constraint on the cost of observations used before the change point and where the post-change distribution is composite. Minimax formulations are proposed for this problem. It is assumed that the post-change family of distributions has a member which is least favorable in a well-defined sense. An algorithm is proposed in which ON–OFF observation control is employed using the least favorable distribution, and a generalized likelihood ratio-based approach is used for change detection. Under additional conditions on the post-change family of distributions, it is shown that the proposed algorithm is asymptotically optimal, uniformly for all possible post-change distributions.

Appraisal of scientific evidence in criminal justice systems: on winds of change and coexisting formats

Interpretation concepts of scientific evidence have always been under discussion among forensic scientists and among all stakeholders of criminal proceedings in general. It seems that this issue has been attracting more attention since the introduction of the case assessment and interpretation (CAI) model in the late nineties and even more since the release of the National Academies of Science report ‘Strengthening Forensic Science in the United States’ in 2009. Following the debates there is, however, a certain danger of overcompensation if the input of stakeholders from e.g. inquisitorial criminal systems is under-represented. Without doubt, a likelihood ratio-based approach can be a powerful tool assisting in logically complex case assessments and judicial considerations of evidence. However, the application of this approach should be an option rather than an international standard as it concerns the concept of the stakeholder’s roles more profoundly in some countries than in others and may possibly take some countries by surprise. In the following article, this is discussed and some proposals are put forward which appear suitable to strengthen the evaluation of forensic results by the principle of methodological pluralism rather than by an exclusive and compulsory commitment to only one approach.

New hypothesis testing-based rapid change detection for power grid system monitoring

The vulnerability of power grid systems to malicious attacks is one of the most pressing problems faced concerning power grid systems. Based on the dynamics of the generators, we show that the time evolution of the power grid system can be modelled by a discrete-time linear state-space model. We employ approaches based on hypothesis testing for failure and intrusion detection of the monitored power grid system. We develop a new locally optimum unknown direction (LOUD) test to detect changes in matrices or vectors and apply this approach to power grid failure and intrusion detection problems. We provide numerical results which show that, unlike the standard generalised likelihood ratio-based approach, the LOUD test is able to produce decisions right after the change has occurred without waiting to collect additional data while it performs nearly as good, within a few percent in the case considered, as the optimum but unachievable likelihood ratio test for the known change. We employ realistic simulations of the IEEE 14 bus system to more fully evaluate the LOUD test.

Searching for first-degree familial relationships in California's offender DNA database: Validation of a likelihood ratio-based approach

A validation study was performed to measure the effectiveness of using a likelihood ratio-based approach to search for possible first-degree familial relationships (full-sibling and parent–child) by comparing an evidence autosomal short tandem repeat (STR) profile to California's ∼1,000,000-profile State DNA Index System (SDIS) database. Test searches used autosomal STR and Y-STR profiles generated for 100 artificial test families. When the test sample and the first-degree relative in the database were characterized at the 15 Identifiler ® (Applied Biosystems ®, Foster City, CA) STR loci, the search procedure included 96% of the fathers and 72% of the full-siblings. When the relative profile was limited to the 13 Combined DNA Index System (CODIS) core loci, the search procedure included 93% of the fathers and 61% of the full-siblings. These results, combined with those of functional tests using three real families, support the effectiveness of this tool. Based upon these results, the validated approach was implemented as a key, pragmatic and demonstrably practical component of the California Department of Justice's Familial Search Program. An investigative lead created through this process recently led to an arrest in the Los Angeles Grim Sleeper serial murders.

Combining spatial data in landslide reactivation susceptibility mapping: A likelihood ratio-based approach in W Belgium

A key issue in landslide susceptibility mapping concerns the relevance of the spatial data combination used in the prediction. Various combinations of high-resolution predictor variables and possibilities of selecting them from a larger dataset are analysed. The scarp reactivation of several landslides in a hilly region of W Belgium is investigated at the pixel scale. The susceptibility modelling uses the reactivated scarp segments as the dependent variable and 13 factors at a 2 m-resolution related to topography, hydrology, land use and lithology as potential independent variables. The modelling uses a likelihood ratio approach based on the comparison, for each independent variable, between two empirical distribution functions (EDFs), respectively for the reactivated and non-reactivated areas. It uses these EDFs as favourability values to build membership values and combine them with a fuzzy Gamma operator. Five different data combinations are tested and compared by analysing the prediction-rate curves obtained by cross-validation. The geomorphological value of the resulting susceptibility maps is also discussed. This research shows relevant results for predicting the susceptibility to scarp reactivation. We highlight the need for testing several data combinations and underline that combining quantitative criteria with expert opinion is an asset for reliable predictions.

System Uncertainty and Statistical Detection for Jump-diffusion Models

Motivated by the common-seen model uncertainty of real-world systems, we propose a likelihood ratio-based approach to statistical detection for a rich class of partially observed systems. Here, the system state is modeled by some jump-diffusion process while the observation is of additive white noise. Our approach can be implemented recursively based on some Markov chain approximation method to compare the competing stochastic models by fitting the observed historical data. Our method is superior to the traditional hypothesis test in both theoretical and computational aspects. In particular, a wide range of different models can be nested and compared in a unified framework with the help of Bayes factor. An illustrating numerical example is also given to show the application of our method.

Exploring the Discriminatory Potential of F0 Distribution Parameters in Traditional Forensic Speaker Recognition

Despite its many prima facie attractive properties for Forensic Speaker Recognition, F0 is regarded as having limited forensic value due to its large within-speaker variability. However, its forensic use to date has been limited mostly to its long-term mean and standard deviation. This paper examines the discriminatory potential, within a Likelihood Ratio-based approach, of additional parametric features from the distribution of long-term F0: its skew, kurtosis, modal F0 and modal density. Motivated by the observation that the shape of the long-term F0 distribution shows less within-speaker occasion-to-occasion difference, we report a forensic discrimination experiment with non-contemporaneous speech samples from 201 male Japanese speakers. Using a multivariate Likelihood Ratio as discriminant distance with the six LTF0 distribution parameters, an equal error rate of 10.7% is obtained from 201 target and 80400 non-target trials. We also investigate how the EER degrades as a function of amount of voiced speech.

Statistical model uncertainty for state-observation models driven by diffusion process

This paper is motivated by the fact that model uncertainty is common-seen in statistics and applied probability. As a response, a likelihood ratio-based approach is presented to statistical detection within a class of state-observation models. Here, the system is driven by some diffusion process while the continuous-time observation process is of additive white noise. The proposed approach can be implemented recursively to compare the competing stochastic systems by fitting the observed historical data. It is superior to the traditional hypothesis test in both theoretical and numerical senses. Moreover, it can be extended to the more general martingale problem setup.

An extended likelihood ratio framework for interpreting evidence

This paper reviews some current methods, the likelihood ratio-based approach and the full Bayesian approach for the interpretation of evidence and discusses previously identified shortcomings in them. It suggests an approach based on a compromise--based on an extended likelihood ratio--that may combine the merits of logic without overstepping acceptable bounds for the forensic scientist in the presentation of evidence. The approach is exposed formally and takes advantage of inferential networks called Bayesian networks.

Technical forensic speaker recognition: Evaluation, types and testing of evidence

Important aspects of Technical Forensic Speaker Recognition, particularly those associated with evidence, are exemplified and critically discussed, and comparisons drawn with generic Speaker Recognition. The centrality of the Likelihood Ratio of Bayes’ theorem in correctly evaluating strength of forensic speech evidence is emphasised, as well as the many problems involved in its accurate estimation. It is pointed out that many different types of evidence are of use, both experimentally and forensically, in discriminating same-speaker from different-speaker speech samples, and some examples are given from real forensic case-work to illustrate the Likelihood Ratio-based approach. The extent to which Technical Forensic Speaker Recognition meets the Daubert requirement of testability is also discussed.

Testing realistic forensic speaker identification in Japanese: a likelihood ratio-based approach using formants

Testing realistic forensic speaker identification in Japanese: a likelihood ratio-based approach using formants