Results 1 to 10 of about 155,766 (268)
Semiparametric Bayesian networks
Information Sciences, 2022 We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
Atienza González, David +2 more
openaire +3 more sources
Bayesian Approach to Linear Bayesian Networks
CoRR, 2023 This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix.
Seyong Hwang +3 more
openaire +2 more sources
Bayesian Networks in Radiology [PDF]
Radiology: Artificial Intelligence, 2023 A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values.
Shawn X. Ma +6 more
openaire +3 more sources
Bayesian Network Classifiers [PDF]
Machine Learning, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nir Friedman +2 more
openaire +2 more sources
Bayesian Neural Networks [PDF]
, 2022 In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network.
Tom Charnock +2 more
openaire +2 more sources
Bayesian networks in reliability [PDF]
Reliability Engineering & System Safety, 2007 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 ...
LANGSETH H, PORTINALE, Luigi
openaire +2 more sources
CoRR, 2023 Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an exploration/exploitation dilemma, a major pathology of frequentist methods. However theoretical understanding of model-free approaches is lacking.
Mattie Fellows +3 more
openaire +3 more sources
Midsized-Rivers/Piebald-Madtom-Bayesian-belief-network: V1.2 Piebald Madtom Bayesian belief network
, 2023 <p>Repository contains supporting files for manuscript, Dunn et al. 2023. (in press at Ecosphere), "Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes." This manuscript presents a ...
Midsized-Rivers
core +1 more source
Efficient utility-based clustering over high dimensional partition spaces [PDF]
, 2009 Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ +9 more
core +1 more source
Granger causality vs. dynamic Bayesian network inference: a comparative study [PDF]
, 2009 Background In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data.
Denby Katherine J +8 more
core +1 more source