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Bayesian inference networks, a synthesis of statistics and skilled strategies, have superior reasoning beneath uncertainty in medicine, enterprise, and social sciences. This progressive amount is the first full treatment exploring how they’re typically utilized to design and analyze revolutionary educational assessments.
Half I develops Bayes nets’ foundations in analysis, statistics, and graph idea, and works by way of the precise-time updating algorithm. Half II addresses parametric varieties for use with analysis, model-checking strategies, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A singular perform is the amount’s grounding in Proof-Centered Design (ECD) framework for analysis design. This “design forward” technique permits designers to take full advantage of Bayes nets’ modularity and means to model difficult evidentiary relationships that come up from effectivity in interactive, technology-rich assessments comparable to simulations. Half III describes ECD, situates Bayes nets as an integral aspect of a principled design course of, and illustrates the ideas with an in-depth check out the BioMass enterprise: An interactive, necessities-based, web-delivered demonstration analysis of science inquiry in genetics.
This book is every a helpful useful resource for professionals in analysis and superior school college students. Its clear exposition, labored-by way of numerical examples, and demonstrations from precise and didactic functions current invaluable illustrations of the best way to make use of Bayes nets in educational analysis. Exercises adjust to each chapter, and the web companion site provides a glossary, data models and disadvantage setups, and hyperlinks to computational belongings.
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