abstract: background: depending on the importance of a good performance appraisal system to achieve organizational strategies, balanced scorecard technique provides a comprehensive foundation for interpreting and translating organization’s vision and strategy in a set of performance measures by regarding an overall view of organization in four perspectives included customer, internal processes,...

1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi +Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed ...

In this paper we present a hierarchical model of linear regression functions in the context of multi–task learning. The parameters of the linear model are coupled by a Dirichlet Process (DP) prior, which implies a clustering of related functions for different tasks. To make approximate Bayesian inference under this model we apply the Bayesian Hierarchical Clustering (BHC) algorithm. The experim...

Hierarchical models are rather common in uncertainty theory. They arise when there is a ‘correct’ or ‘ideal’ (so-called first-order) uncertainty model about a phenomenon of interest, but the modeler is uncertain about what it is. The modeler’s uncertainty is then called second-order uncertainty. For most of the hierarchical models in the literature, both the firstand the second-order models are...

This paper addresses an aspect of sign language (SL) recognition that has largely been overlooked in previous work and yet is integral to signed communication. This work is the most comprehensive to-date on the recognition of the complex variations in sign appearances due to grammatical processes (inflections). These processes systematically modulate both the temporal and spatial dimensions of ...

We propose a new parallel-hierarchical neural network model to enable motor learning for simultaneous control of both trajectory and force. by integrating Hogan's control method and our previous neural network control model using a feedback-error-learning scheme. Furthermore. two hierarchical control laws which apply to the model, are derived by using the Moore-Penrose pseudoinverse matrix. One...