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Probabilistic Graphical Models

Advances in Computer Vision and Pattern Recognition, 2021
The most important problem in machine learning is to estimate and infer the value of unknown variables (e.g., class label) based on the observed evidence (e.g., training samples). Probabilistic models provide a framework that considers learning problems as computing the probability distributions of variables.
Zhi-Hua Zhou
semanticscholar   +7 more sources

Probabilistic graphical models

Machine Learning for Signal Processing, 2019
Statistical 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.
Max A. Little
semanticscholar   +2 more sources

Probabilistic Graphical Models [PDF]

Advances in Computer Vision and Pattern Recognition, 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
L. E. Sucar
semanticscholar   +5 more sources

Probabilistic Graphical Models [PDF]

Encyclopedia of Social Network Analysis and Mining. 2nd Ed., 2018
R. Urtasun, Tamir Hazan, Tti-C, Graphical Models   +3 more
semanticscholar   +3 more sources

Inferring Cellular Networks Using Probabilistic Graphical Models

Science, 2004
N. Friedman
semanticscholar   +3 more sources

33.1 A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models

IEEE International Solid-State Circuits Conference, 2020
Many powerful neural network (NN) models such as probabilistic graphical models (PGMs) and recurrent neural networks (RNNs) require flexibility in dataflow and weight access patterns as shown in Fig.
W. Wan, R. Kubendran, S. Eryilmaz, Wenqiang Zhang, Yan Liao, Dabin Wu, S. Deiss, Bin Gao, Priyanka Raina, Siddharth Joshi, Hua-Qiang Wu, G. Cauwenberghs, H. Wong   +12 more
semanticscholar   +1 more source

Probabilistic Graphical Models of Dyslexia

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naeve Bayes models which differ by their assumptions about the generation process ...
Yair Lakretz, Gal Chechik, Naama Friedmann, Michal Rosen-Zvi   +3 more
openaire   +2 more sources

Survey of Probabilistic Graphical Models

2013 10th Web Information System and Application Conference, 2013
Probabilistic 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
Hongmei Li, Wenning Hao, Wenyan Gan, Gang Chen   +3 more
openaire   +1 more source

THE HUGIN TOOL FOR PROBABILISTIC GRAPHICAL MODELS

International Journal on Artificial Intelligence Tools, 2005
As 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.
Anders L. Madsen, Frank Jensen, Uffe Kjærulff, Michael Lang 0002   +3 more
openaire   +3 more sources

Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA)
This study proposes a method for imbalanced data classification based on deep probabilistic graphical models (DPGMs) to solve the problem that traditional methods have insufficient learning ability for minority class samples.
Yujia Lou, Jie Liu, Yuan Sheng, Jiawei Wang, Yiwei Zhang, Yaokun Ren   +5 more
semanticscholar   +1 more source