A survey on Bayesian network structure learning from data
A necessary step in the development of artificial intelligence is to enable a machine to
represent how the world works, building an internal structure from data. This structure should …
represent how the world works, building an internal structure from data. This structure should …
Large engineering project risk management using a Bayesian belief network
E Lee, Y Park, JG Shin - Expert systems with applications, 2009 - Elsevier
This paper presents a scheme for large engineering project risk management using a
Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six …
Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six …
Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood
Learning Bayesian networks is known to be an NP-hard problem and that is the reason why
the application of a heuristic search has proven advantageous in many domains. This …
the application of a heuristic search has proven advantageous in many domains. This …
A fast PC algorithm for high dimensional causal discovery with multi-core PCs
Discovering causal relationships from observational data is a crucial problem and it has
applications in many research areas. The PC algorithm is the state-of-the-art constraint …
applications in many research areas. The PC algorithm is the state-of-the-art constraint …
Mining causality of network events in log data
S Kobayashi, K Otomo, K Fukuda… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Network log messages (eg, syslog) are expected to be valuable and useful information to
detect unexpected or anomalous behavior in large scale networks. However, because of the …
detect unexpected or anomalous behavior in large scale networks. However, because of the …
Causal discovery in machine learning: Theories and applications.
AR Nogueira, J Gama… - Journal of Dynamics & …, 2021 - search.ebscohost.com
Determining the cause of a particular event has been a case of study for several researchers
over the years. Finding out why an event happens (its cause) means that, for example, if we …
over the years. Finding out why an event happens (its cause) means that, for example, if we …
Bayesian network learning with the PC algorithm: an improved and correct variation
M Tsagris - Applied Artificial Intelligence, 2019 - Taylor & Francis
ABSTRACT PC is a prototypical constraint-based algorithm for learning Bayesian networks,
a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg …
a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg …
[PDF][PDF] A comparison of structural distance measures for causal Bayesian network models
M de Jongh, MJ Druzdzel - … problems of science, computer science series, 2009 - Citeseer
We compare measures of structural distance between both, Bayesian networks and
equivalence classes of Bayesian networks. The main application of these measures is in …
equivalence classes of Bayesian networks. The main application of these measures is in …
Exact learning augmented naive bayes classifier
S Sugahara, M Ueno - Entropy, 2021 - mdpi.com
Earlier studies have shown that classification accuracies of Bayesian networks (BNs)
obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the …
obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the …
Marginal pseudo-likelihood learning of discrete Markov network structures
J Pensar, H Nyman, J Niiranen, J Corander - 2017 - projecteuclid.org
Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures Page 1
Bayesian Analysis (2017) 12, Number 4, pp. 1195–1215 Marginal Pseudo-Likelihood …
Bayesian Analysis (2017) 12, Number 4, pp. 1195–1215 Marginal Pseudo-Likelihood …