Sequential updating of conditional probabilities on directed graphical structures


) can be visualised as the probability of event when the sample space is restricted to event .A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).


Using a real, moderately complex, medical example we illustrate how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief network in this context.

Initially, predictive inference was based on observable parameters and it was the main purpose of studying probability, but it fell out of favor in the 20th century due to a new parametric approach pioneered by Bruno de Finetti.

We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data.

Unifying Markov properties for graphical models (2016).


Estimation of means in graphical Gaussian models with symmetries, Annals of Statistics, 40, 2423-2436, 2012. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems.


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