Burdens of Proof

2021 ◽  
pp. 108-122
Author(s):  
Mark Spottswood
Keyword(s):  
Likelihood Ratio ◽  
Belief Function ◽  
Weight Of Evidence ◽  
Adequate Account ◽  
Probability Threshold ◽  
Normative Account ◽  
Key Questions ◽  
Current Practices

This chapter provides a brief introduction to the scholarly conversation concerning burdens of persuasion. An adequate account of burdens must first explain what case-related facts the burden draws upon to produce outcomes. I review a variety of answers to this question, including probability threshold, likelihood ratio, belief function, weight-of-evidence, explanatory, and story-based approaches. I then identify several key questions that theories must answer with respect to inputs and show that the best answer on any given question must depend on whether the theory is advanced as a psychological, doctrinal, or normative account. The remainder of the chapter considers varying methods of transforming these inputs into case outcomes, including fixed thresholds, variable thresholds, multi-stepped, and continuous approaches. With respect to these choices, the problem of describing current practices is much easier, but the normative debates are harder to resolve.

scholarly journals An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

2015 ◽  
Vol 8 (3) ◽  
pp. 881-891 ◽  
Author(s):  
M. N. Jebur ◽  
B. Pradhan ◽  
H. Z. M. Shafri ◽  
Z. M. Yusoff ◽  
M. S. Tehrany
Keyword(s):  
Natural Hazard ◽  
Model Performance ◽  
Statistical Modelling ◽  
Belief Function ◽  
Spatial Modelling ◽  
Weight Of Evidence ◽  
Microsoft Excel ◽  
Bivariate Statistical Analysis ◽  
Gis Environment ◽  
User Friendly

Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

scholarly journals Likelihood Ratio as Weight of Forensic Evidence: A Closer Look

2017 ◽  
Vol 122 ◽  
Author(s):  
Steven P. Lund ◽  
Hari Iyer
Keyword(s):  
Decision Theory ◽  
Likelihood Ratio ◽  
Quantitative Methods ◽  
Weight Of Evidence ◽  
Bayesian Reasoning ◽  
Bayesian Decision ◽  
Forensic Evidence ◽  
Science Community ◽  
Transfer Of Information ◽  
Personal Decision

The forensic science community has increasingly sought quantitative methods for conveying the weight of evidence. Experts from many forensic laboratories summarize their findings in terms of a likelihood ratio. Several proponents of this approach have argued that Bayesian reasoning proves it to be normative. We find this likelihood ratio paradigm to be unsupported by arguments of Bayesian decision theory, which applies only to personal decision making and not to the transfer of information from an expert to a separate decision maker. We further argue that decision theory does not exempt the presentation of a likelihood ratio from uncertainty characterization, which is required to assess the fitness for purpose of any transferred quantity. We propose the concept of a lattice of assumptions leading to an uncertainty pyramid as a framework for assessing the uncertainty in an evaluation of a likelihood ratio. We demonstrate the use of these concepts with illustrative examples regarding the refractive index of glass and automated comparison scores for fingerprints.

A case study for the integration of predictive mineral potential maps

2014 ◽  
Vol 6 (3) ◽  
Author(s):  
Saro Lee ◽  
Hyun-Joo Oh ◽  
Chul-Ho Heo ◽  
Inhye Park
Keyword(s):  
Likelihood Ratio ◽  
Spatial Association ◽  
Network Models ◽  
Statistical Modelling ◽  
Mineral Deposit ◽  
Weight Of Evidence ◽  
Neural Network Models ◽  
Related Factors ◽  
Mineral Potential ◽  
Potential Map

AbstractThis study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

Youden's Index and the Weight of Evidence

2015 ◽  
Vol 54 (02) ◽  
pp. 198-199 ◽  
Author(s):  
G. Hughes
Keyword(s):  
Likelihood Ratio ◽  
Diagnostic Tests ◽  
Positive Test ◽  
Weight Of Evidence ◽  
Probability Scale ◽  
Log Likelihood ◽  
Log Likelihood Ratio ◽  
Youden’S Index

SummaryBy means of Shannon’s relationship between information and probability, Youden’s index for rating diagnostic tests is shown to be a probability-scale analogue of the log-likelihood ratio of a positive test outcome.

Youden’s Index and the Weight of Evidence Revisited

2015 ◽  
Vol 54 (06) ◽  
pp. 576-577 ◽  
Author(s):  
G. Hughes
Keyword(s):  
Likelihood Ratio ◽  
Diagnostic Tests ◽  
Positive Test ◽  
Weight Of Evidence ◽  
Log Likelihood ◽  
Log Likelihood Ratio ◽  
Youden’S Index

SummaryA correspondence between Youden’s index for rating diagnostic tests and the log-likelihood ratio of a positive test outcome is illustrated by data calculated from two previously-published studies of binary tests.

scholarly journals A response to “Likelihood ratio as weight of evidence: A closer look” by Lund and Iyer

2018 ◽  
Vol 288 ◽  
pp. e15-e19 ◽  
Author(s):  
Simone Gittelson ◽  
Charles E.H. Berger ◽  
Graham Jackson ◽  
Ian W. Evett ◽  
Christophe Champod ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Weight Of Evidence
Sign in / Sign up

Export Citation Format

Share Document