16,662 research outputs found
Dependent Dirichlet Process Rating Model (DDP-RM)
Typical IRT rating-scale models assume that the rating category threshold
parameters are the same over examinees. However, it can be argued that many
rating data sets violate this assumption. To address this practical
psychometric problem, we introduce a novel, Bayesian nonparametric IRT model
for rating scale items. The model is an infinite-mixture of Rasch partial
credit models, based on a localized Dependent Dirichlet process (DDP). The
model treats the rating thresholds as the random parameters that are subject to
the mixture, and has (stick-breaking) mixture weights that are
covariate-dependent. Thus, the novel model allows the rating category
thresholds to vary flexibly across items and examinees, and allows the
distribution of the category thresholds to vary flexibly as a function of
covariates. We illustrate the new model through the analysis of a simulated
data set, and through the analysis of a real rating data set that is well-known
in the psychometric literature. The model is shown to have better
predictive-fit performance, compared to other commonly used IRT rating models.Comment: 2 tables and 5 figure
Compact Markov-modulated models for multiclass trace fitting
Markov-modulated Poisson processes (MMPPs) are stochastic models for fitting empirical traces for simulation, workload characterization and queueing analysis purposes. In this paper, we develop the first counting process fitting algorithm for the marked MMPP (M3PP), a generalization of the MMPP for modeling traces with events of multiple types. We initially explain how to fit two-state M3PPs to empirical traces of counts. We then propose a novel form of composition, called interposition, which enables the approximate superposition of several two-state M3PPs without incurring into state space explosion. Compared to exact superposition, where the state space grows exponentially in the number of composed processes, in interposition the state space grows linearly in the number of composed M3PPs. Experimental results indicate that the proposed interposition methodology provides accurate results against artificial and real-world traces, with a significantly smaller state space than superposed processes
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