In optimal stopping problems, people are asked to choose the maximum out of a sequence of values, under the constraint that a number can only be chosen when it is presented. We present a series of threshold models of human decision making on optimal stopping problems, including a new hierarchical model that assumes individual differences in threshold setting are controlled by deviations or bias...

Numerous Bayesian methods of phenotype prediction and genomic breeding value estimation based on multilocus association models have been proposed. Computationally the methods have been based either on Markov chain Monte Carlo or on faster maximum a posteriori estimation. The demand for more accurate and more efficient estimation has led to the rapid emergence of workable methods, unfortunately ...

Empirical Bayes approach is an attractive method for estimating hyperparameters in hierarchical models. But, under the assumption of normality for a multi-level heteroscedastic hierarchical model, which involves several explanatory variables, the analyst may often wonder whether the shrinkage estimators have efficient asymptotic properties in spite of the fact they involve numerous hyperparamet...

Ecologists often seek to understand patterns and processes across multiple spatial and temporal scales ranging from centimeters to hundreds of meters and from seconds to years. Hierarchical statistical models offer a framework for sampling design and analysis that can be used to incorporate the information collected at finer scales while allowing comparison at coarser scales. In this study we u...

In this paper, we present the results of a longitudinal study for the evolution follow-up in writing among typical pupils in primary education. We propose a method aimed at discovering groups of pupils sharing the same handwriting strategies along their primary education. From the on-line acquisition of writing and drawing tests, writing strategies are modeled by means of a bayesian network. Ex...

We study closures of hierarchical models which are exponential families associated with hypergraphs by decomposing the corresponding interaction spaces in a natural and transparent way. Here, we apply general results on closures of exponential families. Index Terms – Closure of exponential family, graphical model, hierarchical model, interaction order.

To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we ex...

Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be ch...

Fitting multi-parameter models to the behavior of individual participants is a popular approach in cognitive science to measuring individual differences. This approach assumes that the model parameters capture psychologically meaningful and stable characteristics of a person. If so, the estimated parameters should show, to some extent, stability across time. Recently, it has been proposed that ...