This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filte...

Identification of transliterations is aimed at enriching multilingual lexicons and improving performance in various Natural Language Processing (NLP) applications including Cross Language Information Retrieval (CLIR) and Machine Translation (MT). This paper describes work aimed at using the widely applied graphical models approach of ‘Dynamic Bayesian Networks (DBNs) to transliteration identifi...

For about three decades, artificial intelligence has been concerned with a debate on the adequacy of probability for treating uncertainty. The transferable belief model is an alternative framework that resulted from that debate. The peculiarity of the transferable belief model is its dichotomical structure in which a non-Bayesian knowledge representation co-exists with a Bayesian de-

Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approximate inference involves grouping the variables in the process into smaller factors and keeping independent beliefs over these factors. In this paper we present several techniques for decomposing a dynamic Bayesian net...

This paper presents an algorithm to transform a dynamic influence net (DIN) into a dynamic Bayesian network (DBN). The transformation aims to bring the best of both probabilistic reasoning paradigms. The advantages of DINs lie in their ability to represent causal and time-varying information in a compact and easy-to-understand manner. They facilitate a system modeler in connecting a set of desi...

DBNs (Dynamic Bayesian Networks) [1] are powerful tool in modeling time-series data, and have been used in speech recognition recently [2,3,4]. The “decoding” task in speech recognition means to find the viterbi path [5](in graphical model community, “viterbi path” has the same meaning as MPE “Most Probable Explanation”) for a given acoustic observations. In this paper we describe a new algorit...

Switching linear dynamic systems (SLDS) attempt to describe a complex nonlinear dynamic system with a succession of linear models indexed by a switching variable. Unfortunately, despite SLDS’s simplicity exact state and parameter estimation are still intractable. Recently, a broad class of learning and inference algorithms for time-series models have been successfully cast in the framework of d...

Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hidden Markov models and dynamic Bayesian networks (DBNs), use discrete time steps. This paper explores methods for converting DBNs with infinitesimal time steps into DBNs with finite time steps, to enable efficient simu...

Game-based learning offers key advantages for learning through experience in conjunction with offering multi-sensorial and engaging communication. However, ensuring that learning has taken place is the ultimate challenge. Intelligent Tutoring Systems (ITSs) have been incorporated into game-based learning environments to guide learners’ exploration. Emotions have proven to be deeply intertwined ...

MOTIVATION The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of geno...