We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data

are approximately Gaussian. Third, the long-run dynamics of realized logarithmic volatilities are well approximated by a fractionally-integrated long-memory process. Motivated by the three ABDL empirical regularities, we proceed to estimate and evaluate a multivariate model for the logarithmic realized volatilities

environment for Java, called Java PathFinder (JPF), which integrates model checking, program analysis and testing. Part of this work has consisted of building a new Java Virtual Machine that interprets Java bytecode. JPF uses state compression to handle big states, and partial order and symmetry reduction

presents and tests an anchoring and adjustment-based theoretical model of the determinants of system-specific perceived ease of use. The model proposes control (internal and external-conceptualized as computer self-efficacy and facilitating conditions, respectively), intrinsic motivation (conceptualized

Models of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. We argue that much of the reason for this is the lack of an adequate indexing model

model, (b) the derivation of the equations of motion of the system and (c) the choice of an efficient algorithm for the numerical integration of these equations. In polyatomic molecules, the fast internal vibrations are usually decoupled from

This paper presents studies of the coordination of volun-tary human arm movements. A mathematical model is for-mulated which is shown to predict both the qualitative fea-tures and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled

Motivation: Homology models of proteins are of great interest for planning and analyzing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisncing, the authors

the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles.