This article is concerned with a subset of numerically stable and scalable algorithms useful to support computationally complex psychometric models in the era of machine learning and massive data. The subset selected here is a core set of numerical methods that should be familiar to computational psychometricians and considers whitening transforms…

Provides a comprehensive, nontechnical overview of the three maximum likelihood algorithms available for use with missing data and discusses multiple imputation, frequently used in conjunction with the EM algorithm. (SLD)

Describes the maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Describes expectation maximization (EM), generalized expectation maximization, Fletcher-Powell, and Fisher-scoring algorithms for parameter estimation and shows how software can be used to implement each algorithm. (Author/SLD)

Presents a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval parameters. Discusses similarities to language models; the unification of document-oriented and query-oriented views; tests on data sets; algorithms and scalability; and the effectiveness of maximum likelihood…

Proposes an algorithm that minimizes the asymptotic variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. Also shows how the algorithm can be modified if the interest is in a test with a "simple ability structure."…