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Reliable support: Measuring calibration of likelihood ratios

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Cited by

2016Numerical likelihood ratios outputted by LR systems are often based on extrapolation: When to stop extrapolating?56 cited
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2021Forensic science and the principle of excluded middle: “Inconclusive” decisions and the structure of error rate studies38 cited
2020Defence against the modern arts: the curse of statistics—Part II: ‘Score-based likelihood ratios’

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36 cited

2022The strange persistence of (source) “identification” claims in forensic literature through descriptivism, diagnosticism and machinism24 cited

2021Measuring calibration of likelihood-ratio systems: A comparison of four metrics, including a new metric devPAV19 cited

2023From specific-source feature-based to common-source score-based likelihood-ratio systems: ranking the stars16 cited

2021Testing for Calibration Discrepancy of Reported Likelihood Ratios in Forensic Science15 cited

2020Interpretation of Glass Evidence15 cited

2022Objectifying evidence evaluation for gunshot residue comparisons using machine learning on criminal case data14 cited

2020Why calibrating LR-systems is best practice. A reaction to “The evaluation of evidence for microspectrophotometry data using functional data analysis”, in FSI 30513 cited