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Probabilistic graphical models
2019AbstractStatistical machine learning and statistical DSP are built on the foundations of probability theory and random variables. Different techniques encode different dependency structure between these variables. This structure leads to specific algorithms for inference and estimation. Many common dependency structures emerge naturally in this way, as
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Probabilistic Graphical Models Applied to Biological Networks
2021Biological networks can be defined as a set of molecules and all the interactions among them. Their study can be useful to predict gene function, phenotypes, and regulate molecular patterns. Probabilistic graphical models (PGMs) are being widely used to integrate different data sources with modeled biological networks.
Natalia Faraj, Murad +1 more
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Learning Probabilistic Graphical Models
2012Probabilistic Graphical Models (PGM) are a class of statistical models that use a graph structure over a set of variables to encode independence relations between those variables. By augmenting the graph by local parameters, a PGM allows for a compact representation of a joint probability distribution over the variables of the graph, which allows for ...
Juan I. Alonso-Barba +3 more
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Survey of Probabilistic Graphical Models
2013 10th Web Information System and Application Conference, 2013Probabilistic graphical model (PGM) is a generic model that represents the probability-based relationships among random variables by a graph, and is a general method for knowledge representation and inference involving uncertainty. In recent years, PGM provides an important means for solving the uncertainty of intelligent information field, and becomes
Li Hongmei +3 more
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THE HUGIN TOOL FOR PROBABILISTIC GRAPHICAL MODELS
International Journal on Artificial Intelligence Tools, 2005As the framework of probabilistic graphical models becomes increasingly popular for knowledge representation and inference, the need for efficient tools for its support is increasing. The Hugin Tool is a general purpose tool for construction, maintenance, and deployment of Bayesian networks and influence diagrams.
Madsen, A. L. +3 more
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Relational Probabilistic Graphical Models
2015This chapter introduces relational probabilistic graphical models (RPGMs), which combine the expressive power of predicate logic with the uncertain reasoning capabilities of probabilistic graphical models. First, a brief review of propositional and predicate logic is presented.
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Introduction to Probabilistic Graphical Models
2014Abstract Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error ...
Franz Pernkopf +2 more
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Inferring Cellular Networks Using Probabilistic Graphical Models
Science, 2004High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets.
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Advances in Probabilistic Graphical Models
2007In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;contributions to the area are coming from computer science, mathematics, statistics and ...
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