Results 21 to 30 of about 418,262 (313)
Sum–product graphical models [PDF]
Machine Learning, 2019 This paper introduces a new probabilistic architecture called Sum-Product Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence.
Mattia Desana, Christoph Schnörr
openaire +2 more sources
Annals of Medicine, 2023 Background Probabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model.Objective To develop PGM for the prediction of clinical outcome in patients with ...
Dong Ah Shin +6 more
doaj +1 more source
An Additive Graphical Model for Discrete Data
, 2022 We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three-way statistical relation that shares similar properties with conditional independence by ...
Jun Tao (214048) +2 more
core +1 more source
Detecting Community Evolution by Utilizing Individual Temporal Semantics in Social Networks
IEEE Access, 2023 Social networks are becoming increasingly popular and significant. One of the most distinctive features of these networks is their dynamic nature, which means that they change over time.
Feng Wang, Dingbo Hou, Hao Yan
doaj +1 more source
MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS [PDF]
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2011 We investigate a class of multiagent planning problems termed multiagent expedition, where agents move around an open, unknown, partially observable, stochastic, and physical environment, in pursuit of multiple and alternative goals of different utility. Optimal planning in multiagent expedition is highly intractable.
Xiang, Yang, Hanshar, Frank
openaire +2 more sources
Graph-based multivariate conditional autoregressive models
Statistical Theory and Related Fields, 2019 The conditional autoregressive model is a routinely used statistical model for areal data that arise from, for instances, epidemiological, socio-economic or ecological studies.
Ye Liang
doaj +1 more source
Partition-Merge: Distributed Inference and Modularity Optimization
IEEE Access, 2021 This paper presents a novel meta-algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nutshell, PM divides the graph into small subgraphs using our novel randomized
Vincent Blondel +4 more
doaj +1 more source
A Tighter Bound for Graphical Models [PDF]
Neural Computation, 2001 We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field.
Leisink, M.A.R., Kappen, H.J.
openaire +5 more sources
DragDL:An Easy-to-Use Graphical DL Model Construction System [PDF]
Jisuanji kexue, 2021 Deep learning has broad applications in various fields.However,users still need to face problems from two aspects when applying deep learning.First,deep learning has a complex theoretical background,non-professional users lack background knowledge in ...
TANG Shi-zheng, ZHANG Yan-feng
doaj +1 more source
Lifted graphical models: a survey [PDF]
Machine Learning, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kimmig, Angelika +2 more
openaire +4 more sources