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Extraction Of Information From Crowdsourcing: Experimental Test Employing Bayesian, Maximum Likelihood, And Maximum Entropy Methods, Mark P. Silverman
Extraction Of Information From Crowdsourcing: Experimental Test Employing Bayesian, Maximum Likelihood, And Maximum Entropy Methods, Mark P. Silverman
Faculty Scholarship
A crowdsourcing experiment in which viewers (the “crowd”) of a British Broadcasting Corporation (BBC) television show submitted estimates of the number of coins in a tumbler was shown in an antecedent paper (Part 1) to follow a log-normal distribution ∧(m,s2). The coin-estimation experiment is an archetype of a broad class of image analysis and object counting problems suitable for solution by crowdsourcing. The objective of the current paper (Part 2) is to determine the location and scale parameters (m,s) of ∧(m,s2) by both Bayesian and maximum …
Crowdsourced Sampling Of A Composite Random Variable: Analysis, Simulation, And Experimental Test, Mark P. Silverman
Crowdsourced Sampling Of A Composite Random Variable: Analysis, Simulation, And Experimental Test, Mark P. Silverman
Faculty Scholarship
A composite random variable is a product (or sum of products) of statistically distributed quantities. Such a variable can represent the solution to a multi-factor quantitative problem submitted to a large, diverse, independent, anonymous group of non-expert respondents (the “crowd”). The objective of this research is to examine the statistical distribution of solutions from a large crowd to a quantitative problem involving image analysis and object counting. Theoretical analysis by the author, covering a range of conditions and types of factor variables, predicts that composite random variables are distributed log-normally to an excellent approximation. If the factors in a problem …