This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.

Over the last decade, Bayesian Networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications, and point to ongoing research that is relev...

Multiply sectioned Bayesian networks (MSBNs) provide one framework for agents to estimate the state of a domain. Existing methods for multi-agent inference in MSBNs are based on linked junction forests (LJFs). The methods are extensions of message passing in junction trees for inference in singleagent Bayesian networks (BNs). We consider extending other inference methods in single-agent BNs to ...

Bayesian learning of belief networks (BLN) is a method for automatically constructing belief networks (ENs) from data using search and Bayesian scoring techniques. K2 is a particular instantiation of the method that implements a greedy search strategy. To evaluate the accuracy of K2, we randomly generated a number of BNs and for each of those we simulated data sets. K2 was then used to induce t...

This paper introduces integer Bayesian networks (BNs), i.e. BNs with discrete valued nodes where parameters are stored as integer numbers. These networks allow for efficient implementation in hardware while maintaining a (partial) probabilistic interpretation under scaling. An algorithm for the computation of margin maximizing integer parameters is presented and its efficiency is demonstrated. ...

Over the last decade, Bayesian Networks (BNs) have become a popular tool for modelling many kinds of statistical problems. In this chapter we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications. This discussion is closely linked to the analysis of a real-world example.

Bayesian networks (BNs) are rapidly becoming a leading tool in applied Artificial Intelligence (AI). BNs may be built by eliciting expert knowledge or learned via causal discovery programs. A hybrid approach is to incorporate prior information elicited from experts into the causal discovery process. We present several ways of using expert information as prior probabilities in the CaMML causal d...

Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The computational complexity of inference, however, hinders its applicability to many real-world domains that in principle can be modeled by BNs. Inference methods based on Weighted Model Counting (WMC) reduce the cost of inference by exploiting patterns exhibited by the probabilities associated with BN nodes. ...

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directed acyclic graph. In BNs, the conditional relationships between parent and child variables are pro...

A multi-valued Non-Impeding Noisy-AND (NIN-AND) tree model has linear complexity and is more expressive than several Causal Independence Models (CIMs) for expressing Conditional Probability Tables (CPTs) in Bayesian Networks (BNs). We show that it is also more general than the well-known noisy-MAX. To exploit NIN-AND tree models in inference, we develop a sound Multiplicative Factorization (MF)...