Hierarchical Models: A Current Computational Perspective
نویسنده
چکیده
Hierarchical models (HMs) provide a flexible framework for modeling data. The ongoing development of techniques like the EM algorithm and Markov chain Monte Carlo has enabled statisticians to make use of increasingly more complicated HMs over the last few decades. In this article, we consider Bayesian and frequentist versions of a general, two-stage HM, and describe several examples from the literature that illustrate its versatility. Some key aspects of the computational techniques that are currently used in conjunction with this HM are then examined in the context of McCullagh and Nelder’s (1989) salamander data. Several areas that are ripe for new research are identified.
منابع مشابه
Multi-perspective Decision Support System for Hierarchical Bus Transportation Network Design: Tehran Case Study
In this paper, a multi-perspective decision support system (MP-DSS) to design hierarchical public transportation network is developed. Since this problem depends on different perspectives, MP-DSS consists of two sub-systems with macro and micro sub-systems based on travel information, land use and expert knowledge. In the micro sub-system, two sub-modules are developed considering o...
متن کاملHierarchical models for repeated binary data using the IBF sampler
Hierarchical models have emerged as a promising tool for the analysis of repeated binary data. However, the computational complexity in these models have limited their applications in practice. Several approaches have been proposed in the literature to overcome the computational difficulties including maximum likelihood estimation from a frequentist perspective (e.g., J.Amer. Statist.Assoc. 89 ...
متن کاملComplete / Incomplete Hierarchical Hub Center Single Assignment Network Problem
In this paper we present the problem of designing a three level hub center network. In our network, the top level consists of a complete network where a direct link is between all central hubs. The second and third levels consist of star networks that connect the hubs to central hubs and the demand nodes to hubs and thus to central hubs, respectively. We model this problem in an incomplete net...
متن کاملTrajectory tracking of under-actuated nonlinear dynamic robots: Adaptive fuzzy hierarchical terminal sliding-mode control
In recent years, underactuated nonlinear dynamic systems trajectory tracking, such as space robots and manipulators with structural flexibility, has become a major field of interest due to the complexity and high computational load of these systems. Hierarchical sliding mode control has been investigated recently for these systems; however, the instability phenomena will possibly occur, especia...
متن کاملStochastic Optimality Theory, local search, and Bayesian learning of hierarchical models
The Gradual Learning Algorithm (GLA) (Boersma and Hayes, 2001) can be seen as a stochastic local search method for learning Stochastic OT grammars. This paper tries to achieve the following goals: first, in response to the criticism in (Keller and Asudeh, 2002), we point out that the computational problem of learning stochastic grammars does have a general approximate solution (Lin, 2005). Seco...
متن کامل