Keyword-based search returns its results without concern for the information needs of users. In general, search queries are too short to represent what users want, and thus it is necessary to represent users’ intended semantics more accurately. Our goal is to enrich the semantics of user-specific information (e.g., users’ queries and preferences) and documents with their corresponding concepts ...

We present the infinite dynamic Bayesian network model (iDBN), a nonparametric, factored state-space model that generalizes dynamic Bayesian networks (DBNs). The iDBN can infer every aspect of a DBN: the number of hidden factors, the number of values each factor can take, and (arbitrarily complex) connections and conditionals between factors and observations. In this way, the iDBN generalizes o...

Modelling segment duration in text-to-speech systems is hindered by the database imbalance and factor interaction problems. We propose a probabilistic Bayesian belief network (BN) approach to overcome data sparsity and factor interaction problems. The belief network approach makes good estimations in cases of missed or incomplete data. Also, it captures factor interaction in a concise way of ca...

Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidden units. The Indian buffet process has been used as a nonparametric Bayesian prior on the structure of a directed belief network with a single infinitely wide hidden layer. Here, we introduce the cascading Indian buff...

Belief Networks in the Bayesian approach provide a wellestablished methodology to fuse prior knowledge and statistical observations for an enriched decision support. In this paper we investigate one of the advantages of the Bayesian approach the provided additional uncertainty information for predictions in a medical classification problem. We perform a Bayesian analysis using Belief Network mo...

Statistical models of natural images provide an important tool for researchers in the fields of machine learning and computational neuroscience. The canonical measure to quantitatively assess and compare the performance of statistical models is given by the likelihood. One class of statistical models which has recently gained increasing popularity and has been applied to a variety of complex da...

Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires eeective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. We describe several methods for automatic generation of qualitative verbal explanations in sy...

This paper presents an eficient algorithm for learning a Bayesian belief network (BBN) structure from a database, as well as providing a comparison between two BBN structure fitness functions. A Bayesian belief network is a directed acyclic graph representing conditional expectations. In this paper, we propose a two-phase algorithm. The first phase uses asymptotically correct structure learning...

In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms. Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian n...