This paper presents a new approach to inference in Bayesian networks with Boolean variables. The principal idea is to encode the network by logical sentences and to compile the resulting CNF into a deterministic DNNF. From there, all possible queries are answerable in linear time relative to its size. This makes it a potential solution for real-time applications of probabilistic inference with limited computational resources. The underlying idea is similar to Darwiche’s differential approach to inference in Bayesian networks, but the core of the proposed CNF encoding is slightly different. This alternative encoding enables a more intuitive and elegant solution, which is apparently more efficient
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Abstract. We present Bayesian Description Logics (BDLs): an exten-sion of Description Logics (DLs) w...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
We present a method for dynamically generating Bayesian networks from knowledge bases consisting of ...
We present a method for dynamically constructing Bayesian networks from knowledge bases consisting o...
Several models combining Bayesian networks with logic exist. The two most developed models are Proba...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a co...
Compiling Bayesian networks (BNs) is a hot topic within probabilistic modeling and processing. In th...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Compiling Bayesian networks (BNs) is a hot topic within probabilistic modeling and processing. In th...
Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The analysis of...
Abstract. This paper addresses an algorithm for probabilistic inference for Bayesian Networks (BNs)....
We study the problem of reasoning in the probabilistic De-scription Logic BEL. Using a novel structu...
Probabilistic logical models have proven to be very successful at modelling uncertain, complex relat...
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Abstract. We present Bayesian Description Logics (BDLs): an exten-sion of Description Logics (DLs) w...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
We present a method for dynamically generating Bayesian networks from knowledge bases consisting of ...
We present a method for dynamically constructing Bayesian networks from knowledge bases consisting o...
Several models combining Bayesian networks with logic exist. The two most developed models are Proba...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a co...
Compiling Bayesian networks (BNs) is a hot topic within probabilistic modeling and processing. In th...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Compiling Bayesian networks (BNs) is a hot topic within probabilistic modeling and processing. In th...
Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The analysis of...
Abstract. This paper addresses an algorithm for probabilistic inference for Bayesian Networks (BNs)....
We study the problem of reasoning in the probabilistic De-scription Logic BEL. Using a novel structu...
Probabilistic logical models have proven to be very successful at modelling uncertain, complex relat...
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Abstract. We present Bayesian Description Logics (BDLs): an exten-sion of Description Logics (DLs) w...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...