Network inference with hidden units

DOI10.3934/mbe.2014.11.149zbMath1279.62201arXiv1301.7274OpenAlexW2963564146WikidataQ44630009 ScholiaQ44630009MaRDI QIDQ395718

John Hertz, Joanna Tyrcha

Publication date: 30 January 2014

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.7274

zbMATH Keywords

latent variablesmean field theorynetwork inferencehidden unitskinetic Ising models

Mathematics Subject Classification ID

General nonlinear regression (62J02) Dynamic lattice systems (kinetic Ising, etc.) and systems on graphs in time-dependent statistical mechanics (82C20) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items

The appropriateness of ignorance in the inverse kinetic Ising model ⋮ Inferring hidden states in a random kinetic Ising model: replica analysis ⋮ Belief propagation and replicas for inference and learning in a kinetic Ising model with hidden spins ⋮ Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves ⋮ Inference for dynamics of continuous variables: the extended Plefka expansion with hidden nodes

Cites Work

• Estimating the dimension of a model
• Collective properties of neural networks: A statistical physics approach
• Bayesian Reasoning and Machine Learning
• Time-Dependent Statistics of the Ising Model
• A new look at the statistical model identification
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