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An Introduction to Probabilistic Graphical Models
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceThis tutorial covers an introduction to Probabilistic Graphical Models (PGM), such as Bayesian Networks and Markov Random Fields, for reasoning under uncertainty in intelligent systems.
Luigi Portinale
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On a hypergraph probabilistic graphical model [PDF]
Annals of Mathematics and Artificial Intelligence, 2020 We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more ...
Mohammad Ali Javidian +3 more
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Extending Stan for Deep Probabilistic Programming [PDF]
, 2020 Stan is a popular declarative probabilistic programming language with a high-level syntax for expressing graphical models and beyond. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro.
Baudart, Guillaume +5 more
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Entropy, 2020 Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning.
Zhigao Guo, Anthony C. Constantinou
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Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models
Mathematics, 2022 The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision ...
Pedro Bonilla-Nadal +4 more
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Probabilistic Graphical Models [PDF]
, 2015 In this chapter, we will briefly summarize the basic concepts of probability as well as graph theory.We start with important terms and definitions from graph theory and emphasize the relation to our hierarchical models. Directed and undirected graphs will be introduced and compared with each other. Then, we will give an overview of the random variables
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Computation of Kullback–Leibler Divergence in Bayesian Networks
Entropy, 2021 Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q.
Serafín Moral +2 more
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Recent advances in imprecise-probabilistic graphical models [PDF]
, 2012 We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities.
De Bock, Jasper +2 more
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Bayesian Test of Significance for Conditional Independence: The Multinomial Model
Entropy, 2014 Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models.
Pablo de Morais Andrade +2 more
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Inference Attacks on Genomic Data Based on Probabilistic Graphical Models
Big Data Mining and Analytics, 2020 The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information, leading to one of the most important concerns — genomic data privacy. Since personal biomedical data are highly
Zaobo He, Junxiu Zhou
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