Discussion of text compression focuses on a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Highlights include probability vectors; algorithms; implementation details; and experimental data with natural languages. (Author/LRW)

Single time latent class models are extended to situations in which measurements are repeated across time. The static approach is extended using multiple indicator Markov chain models. Problems associated with these models are discussed, and their applicability is demonstrated with data from a longitudinal study on solving arithmetic word…

Illustrates that sampling variance can be very large when a three-wave quasi simplex model is used to obtain reliability estimates. Also shows that, for the reliability parameter to be identified, the model assumes a Markov process. These problems are evaluated with both real and Monte Carlo data. (SLD)

Markov models provide a general framework for analyzing and interpreting time dependencies in psychological applications. Recent work extended Markov models to the case of latent states because frequently psychological states are not directly observable and subject to measurement error. This article presents a further generalization of latent…

This paper develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different achievement levels. The model specifies interactions between teacher effects and students' predicted scores on a test, estimating both average effects of individual teachers and interaction terms…

This paper asserts that causality is an intriguing but controversial topic in philosophy, statistics, and educational and psychological research. By supporting the Causal Markov Condition and the faithfulness condition, Clark Glymour attempted to draw causal inferences from structural equation modeling. According to Glymour, in order to make…

Shows how to characterize language learning in a finite parameter space, such as in the "principles-and-parameters" approach, as a Markov structure. Explains how sample complexity varies with input distributions and learning regimes. Finds that a simple random-step algorithm always converges to the right target language and works faster than a…

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative inter-individual differences and auto-dependencies are accounted for by assuming within-subject variability with respect to the…

An approach called generalizability in item response modeling (GIRM) is introduced in this article. The GIRM approach essentially incorporates the sampling model of generalizability theory (GT) into the scaling model of item response theory (IRT) by making distributional assumptions about the relevant measurement facets. By specifying a random…

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

A class of multivariate exponential distributions is defined as the distributions of occupancy times in upwards skip-free Markov processes in continuous time. These distributions are infinitely divisible, and the multivariate gamma class defined by convolutions and fractions is a substantial generalization of the class defined by N. L. Johnson and…

Coefficient alpha is a simple and very useful index of test reliability that is widely used in educational and psychological measurement. Classical statistical inference for coefficient alpha is well developed. This paper presents two methods for Bayesian statistical inference for a single sample alpha coefficient. An approximate analytic method…

Suggests that forecasting the usable life of new audiovisual equipment by standard statistical techniques is error prone and often misleading. Claims that Markov analysis allows the user to obtain a clear indication of the typical progression of equipment from being new to be being no longer economically repairable. (RS)

Demonstrates Markov chain Monte Carlo (MCMC) techniques that are well-suited to complex models with Item Response Theory (IRT) assumptions. Develops an MCMC methodology that can be routinely implemented to fit normal IRT models, and compares the approach to approaches based on Gibbs sampling. Contains 64 references. (SLD)

This study proposes a probabilistic model for automatically extracting English noun phrases for indexing or information retrieval. The technique is based on a Markov model, whose initial parameters are estimated by a phrase lookup program with a phrase dictionary, then optimized by a set of maximum entropy parameters. (Author/LRW)