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Structure Learning in Graphical Modeling
[article]
2016
arXiv
pre-print
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More recently, applications such as reconstructing gene regulatory networks from gene expression data have driven major advances in structure learning, that is, estimating the graph underlying a model. ...
A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest. ...
Learning Directed Graphical Models We now consider learning the structure of a directed graphical model. ...
arXiv:1606.02359v1
fatcat:7q2cihtqnndi7aqzppgs3jqajm
Local Structure Learning in Graphical Models
[chapter]
2003
Planning Based on Decision Theory
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A topic in probabilistic network learning is to exploit local network structure, i.e., to capture regularities in the conditional probability distributions, and to learn networks with such local structure ...
In this paper we present a modification of the learning algorithm for Bayesian networks with a local decision graph representation suggested in Chickering et al. (1997) , which is often more efficient. ...
Such independence relations have been studied extensively in the field of graphical modeling Kruse et al. (1991) and though using them to facilitate reasoning in multidimensional domains has originated ...
doi:10.1007/978-3-7091-2530-4_7
fatcat:bio7jaq5gjdwdctn4xttg5i7nq
Bayesian structure learning in graphical models
2015
Journal of Multivariate Analysis
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Gaussian graphical models provide an important tool in describing conditional independence through presence or absence of the edges in the underlying graph. ...
Hence the resulting posterior distribution can be used for graphical structure learning. The posterior convergence rate of the precision matrix is obtained. ...
We find the median probability model as selected by the Bayesian graphical structure learning method. The corresponding graphical structure is displayed in Figure 1 . ...
doi:10.1016/j.jmva.2015.01.015
fatcat:k644bzluzjfetmkmw4wtuxsdre
Unifying learning in games and graphical models
2005
2005 7th International Conference on Information Fusion
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mechanisms in games and graphical models. ...
This, in turn, is the dominant characteristic of probabilistic learning in graphical models which, however, lack a natural decentralised formulation. ...
VARIATIONAL LEARNING The Kullback-Leibler minimisation criterion is well known and used in machine learning, for example in the estimation of probabilistic models, known as graphical models. ...
doi:10.1109/icif.2005.1591992
fatcat:hh4uykv2tfdqhckrxnfky5c6mm
Variational Learning in Graphical Models and Neural Networks
[chapter]
1998
ICANN 98
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Variational methods are becoming increasingly popular for inference and learning in probabilistic models. ...
In this paper we review the underlying framework of variational methods and discuss example applications involving sigmoid belief networks, Boltzmann machines and feed-forward neural networks. ...
provide a new framework for inference and learning in probabilistic models, which complement previous approaches and offer some specific advantages. ...
doi:10.1007/978-1-4471-1599-1_2
fatcat:wwba75whkneo7fvdaf75xvuu44
Learning as MAP Inference in Discrete Graphical Models
2012
Neural Information Processing Systems
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deep belief networks, our framework entails a non-convex but discrete formulation, where estimation amounts to finding a MAP configuration in a graphical model whose potential functions are low-dimensional ...
By reducing the learning problem to a MAP inference problem, we can immediately translate the guarantees available for many inference settings to the learning problem itself. ...
Therefore we can resort to the vast literature on inference in graphical models to find exact or approximate solutions for (7) . ...
dblp:conf/nips/LiuPC12
fatcat:g72l6g36pbhjhjqjuaj62qixxa
Model-Based Reinforcement Learning in Differential Graphical Games
2018
IEEE Transactions on Control of Network Systems
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2017 pre-print arXiv:1702.08584v1 fatcat:e4fmpzdhu5e5hh6p3yxwicgyze
A model-based reinforcement learning technique is developed to cooperatively control a group of agents to track a trajectory in a desired formation. ...
This paper seeks to combine differential game theory with the actor-critic-identifier architecture to determine forward-in-time, approximate optimal controllers for formation tracking in multi-agent systems ...
However, computation of δ i in (22) and u ij in (7) requires exact model knowledge. ...
doi:10.1109/tcns.2016.2617622
fatcat:pdzgajl4kneybixrb2irvln7re
Learning block structured graphs in Gaussian graphical models
[article]
2022
arXiv
pre-print
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Within the framework of Gaussian graphical models, a prior distribution for the underlying graph is introduced to induce a block structure in the adjacency matrix of the graph and learning relationships ...
The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional independence structure. ...
When such a family of probability distributions is chosen to be Gaussian, those models are known as Gaussian graphical models (Lauritzen 1996) . ...
arXiv:2206.14274v2
fatcat:cisln76m5nfq7a5orponwgc7fq
Variational Learning in Mixed-State Dynamic Graphical Models
[article]
2013
arXiv
pre-print
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We present a mixed-state dynamic graphical model in which a hidden Markov model drives a linear dynamic system. ...
The number of computations needed for exact inference is exponential in the sequence length, so we derive an approximate variational inference technique that can also be used to learn the parameters of ...
Acknowledgments This work was supported in part by National Sci ence Foundation grant IRI-96-34618 and in part by the Army Research Laboratory Cooperative Agree- ...
arXiv:1301.6731v1
fatcat:ju24ji3gzjbi5hkg7d62jd22bi
Bayesian Structure Learning in Sparse Gaussian Graphical Models
2015
Bayesian Analysis
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One approach to this problem is Gaussian graphical modeling, which describes conditional independence of variables through the presence or absence of edges in the underlying graph. ...
In this paper, we introduce a novel and efficient Bayesian framework for Gaussian graphical model determination which is a trans-dimensional Markov Chain Monte Carlo (MCMC) approach based on a continuous-time ...
In this paper, we focus on Bayesian structure learning in Gaussian graphical models for both decomposable and non-decomposable cases. ...
doi:10.1214/14-ba889
fatcat:2cgpsfwlqvf2xax5kgaa5ra4uy
Structure Learning in Graphical Models from Indirect Observations
[article]
2022
arXiv
pre-print
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We derive the minimum sample number n and dimension d as n≳ (deg)^4 log^4 n and d ≳ p + (deg·log(d-p))^β/4, respectively, where deg is the maximum Markov blanket in the graphical model and β > 0 is some ...
This paper considers learning of the graphical structure of a p-dimensional random vector X ∈ R^p using both parametric and non-parametric methods. ...
The most related works on the nonparametric learning of graphical models are (Liu et al., 2009; 2011; Zhao et al., 2014; Xu & Gu, 2016; Fan et al., 2017) . ...
arXiv:2205.03454v1
fatcat:hzrbkdh3zrebxgxk5sedfdwhee
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models
[article]
2021
arXiv
pre-print
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Gaussian graphical models are relevant tools to learn conditional independence structure between variables. ...
In this class of models, Bayesian structure learning is often done by search algorithms over the graph space. ...
In that case, we perform structure learning on graphical models with up to 150 variables and obtain the good results of Section 6. ...
arXiv:1706.04416v3
fatcat:7xuem3tc4fexjmjy75ezr4x32q
Usage of 3D Computer Modelling in Learning Engineering Graphics
[chapter]
2016
Virtual Learning
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Essentially, graphic communication, which is done via engineering drawings and models, is the clean, practical language with defined rules that need to be overcome if one wants to be successful in engineering ...
Visualization, sketching, modelling and preparation of technical documentation are ways in which engineers and technologists communicate in creating new products and structures in the modern technical ...
Engineering graphics and 3D solid modelling, the two basic methods of design underrepresented in use today, are shown in Figure 1 . ...
doi:10.5772/65217
fatcat:nbtg2pkjq5e4pnqq7ckw3aapda
Neural Variational Inference and Learning in Undirected Graphical Models
[article]
2017
arXiv
pre-print
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Many problems in machine learning are naturally expressed in the language of undirected graphical models. ...
Here, we propose black-box learning and inference algorithms for undirected models that optimize a variational approximation to the log-likelihood of the model. ...
Introduction Many problems in machine learning are naturally expressed in the language of undirected graphical models. ...
arXiv:1711.02679v2
fatcat:ozbyqcnqbze7pn7tr6n7cp5iua
Fast Algorithms for Learning Latent Variables in Graphical Models
[article]
2017
arXiv
pre-print
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We study the problem of learning latent variables in Gaussian graphical models. ...
We introduce fast, proper learning algorithms for this problem. In contrast with existing approaches, our algorithms are manifestly non-convex. ...
ACKNOWLEDGEMENTS This work was supported in part by grants from the National Science Foundation and NVIDIA. ...
arXiv:1706.08936v2
fatcat:gusstelgbrhpnj6rseydjqbtsq
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