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Everything about Bayesian network
Probabilistic graphical representation of causal relationships
A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Bayesian networkA 5-minute orientation
- Skim a recent paper: Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning UsersInfluential work since 2022
- Pick up a classic book: Bayesian artificial intelligenceThe most-reprinted book on the subject
- Read the landmark paper: Bayesian Network ClassifiersCited 3,813 times
- Go sideways: Conditional random fieldA closely related topic
- Go sideways: Graphical modelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Bayesian Network Classifiers
Good practice in Bayesian network modelling
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
Discrete Bayesian Network Classifiers
Bayesian Approach to Network Modularity
Network control by Bayesian broadcast
Bayesian Confidence Propagation Neural Network
Network Plasticity as Bayesian Inference
🆕 Influential since 2022
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
A survey of Bayesian Network structure learning
A quantitative framework for network resilience evaluation using Dynamic Bayesian Network
Bayesian neural network and Bayesian physics-informed neural network via variational inference for seismic petrophysical inversion
Combinatorial Techniques for Fault Diagnosis in Nuclear Power Plants Based on Bayesian Neural Network and Simplified Bayesian Network-Artificial Neural Network
📚 Books worth reading
Bayesian artificial intelligenceKevin B. Korb · 2003
Bayesian networksTimo Koski · 2009
The Anatomy of BiasJan Lauwereyns · 2011
Bayesian learning for neural networksRadford M. Neal · 1995
Statistical And Evolutionary Analysis Of Biological NetworksMichael P. H. Stumpf · 2009- 📕Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor NetworksYunfei Xu, Choi Jongeun · 2015
Advances in probabilistic graphical modelsLucas, Peter · 2007
Learning Bayesian networksRichard E. Neapolitan · 2003
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Conditional random field In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction…
- Graphical model A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional…
- Gibbs sampling In statistics, Gibbs sampling or a Gibbs sampler, also known in statistical mechanics as the heat bath algorithm, is a Markov chain Monte Carlo (MCMC)…
- Dependency network (graphical model) Dependency networks (DNs) are graphical models, similar to Markov networks, wherein each vertex (node) corresponds to a random variable and each edge captures…
- Bayesian inference Bayesian inference (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to calculate a probability…
- Belief propagation Belief propagation, also known as sum–product message passing, is a message-passing algorithm for performing inference on graphical models, such as Bayesian…
- Graphoid A graphoid is a set of statements of the form, "X is irrelevant to Y given that we know Z" where X, Y and Z are sets of variables. The notion of "irrelevance"…
- Posterior probability The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood…