scholarly journals A complete method for assessing the effectiveness of eyewitness identification procedures: Expected information gain.

2021 ◽  
Author(s):  
Jeffrey J. Starns ◽  
Andrew L. Cohen ◽  
Caren M. Rotello
Keyword(s):  
Eyewitness Identification ◽  
Information Gain ◽  
Expected Information ◽  
Expected Information Gain ◽  
Identification Procedures

scholarly journals A Complete Method for Assessing the Effectiveness of Eyewitness Identification Procedures: Expected Information Gain

2021 ◽  
Author(s):  
Jeffrey Joseph Starns ◽  
Andrew L. Cohen ◽  
Caren M. Rotello
Keyword(s):  
Information Theory ◽  
Eyewitness Identification ◽  
Information Gain ◽  
Relative Effectiveness ◽  
Policy Recommendations ◽  
Identification Performance ◽  
Expected Information ◽  
New Perspective ◽  
Expected Information Gain ◽  
Identification Procedures

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the Expected Information Gain (EIG) for an identification attempt, a single value that summarizes an identification procedure’s overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the ROC) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight key distinctions between existing measures and EIG. An overarching theme is that EIG provides a complete measure of evidentiary value, in the sense that it factors in all aspects of identification performance. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research and provide a new perspective on policy recommendations for the use of identifications in real investigations.

scholarly journals A Complete Method for Assessing the Effectiveness of Eyewitness Identification Procedures: Expected Information Gain

2021 ◽  
Author(s):  
Jeffrey Joseph Starns ◽  
Andrew L. Cohen ◽  
Caren M. Rotello
Keyword(s):  
Information Theory ◽  
Eyewitness Identification ◽  
Information Gain ◽  
Relative Effectiveness ◽  
Criminal Investigations ◽  
Expected Information ◽  
Expected Information Gain ◽  
Blind Spots ◽  
Identification Procedures

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the Expected Information Gain (EIG) for an identification attempt, a single value that summarizes an identification procedure’s overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of theoretical demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the ROC) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight “blind spots” of the existing measures as a contrast to EIG, which considers every factor relevant to a procedure’s potential for decreasing uncertainty about guilt or innocence. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research. For research designed to identify procedures that will be most effective in criminal investigations, EIG supersedes all other measures, on both theoretical and practical grounds.

scholarly journals Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 258
Author(s):  
Zhihang Xu ◽  
Qifeng Liao
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Information Gain ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Likelihood Functions ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Bayesian Monte Carlo ◽  
Expected Information Gain

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.

scholarly journals Visual attention maximizes expected information gain in goal inference

2021 ◽  
Vol 21 (9) ◽  
pp. 2187
Author(s):  
Bohao Shi ◽  
Zhen Li ◽  
Yazhen Peng ◽  
Zhuoxuan Liu ◽  
Jifan Zhou ◽  
...  
Keyword(s):  
Visual Attention ◽  
Information Gain ◽  
Expected Information ◽  
Expected Information Gain ◽  
Goal Inference

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Piyush Pandita ◽  
Ilias Bilionis ◽  
Jitesh Panchal
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Information Gain ◽  
Black Box ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Physical Response ◽  
Hypothetical Experiment ◽  
Statistical Expectation ◽  
Expected Information Gain

Abstract Bayesian optimal design of experiments (BODEs) have been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE methods operate in specific contexts like optimization, or learning a universal representation of the black-box function. The objective of this paper is to design a BODE for estimating the statistical expectation of a physical response surface. This QoI is omnipresent in uncertainty propagation and design under uncertainty problems. Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI. We represent the information gain from a hypothetical experiment as the Kullback–Liebler (KL) divergence between the prior and the posterior probability distributions of the QoI. The prior distribution of the QoI is conditioned on the observed data, and the posterior distribution of the QoI is conditioned on the observed data and a hypothetical experiment. The main contribution of this paper is the derivation of a semi-analytic mathematical formula for the expected information gain about the statistical expectation of a physical response. The developed BODE is validated on synthetic functions with varying number of input-dimensions. We demonstrate the performance of the methodology on a steel wire manufacturing problem.

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