scientific article; zbMATH DE number 6860774

zbMath1440.62320arXiv1402.5715MaRDI QIDQ4636975

Samuel J. Gershman, Vikash K. Mansinghka, Ardavan Saeedi, Tejas Kulkarni

Publication date: 17 April 2018

Full work available at URL: https://arxiv.org/abs/1402.5715

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

zbMATH Keywords

hidden Markov modelvariational methodsIsing modelBayesian inferenceDirichlet process mixture modelinfinite relational model

Mathematics Subject Classification ID

Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Disordered systems (random Ising models, random Schrödinger operators, etc.) in equilibrium statistical mechanics (82B44) Sequential statistical analysis (62L10)

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Variational Bayesian analysis for Wiener degradation model with random effects ⋮ Exact or approximate inference in graphical models: why the choice is dictated by the treewidth, and how variable elimination can be exploited

Uses Software

• HdBCS

Cites Work

• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Rates of convergence of the Hastings and Metropolis algorithms
• Experiments in stochastic computation for high-dimensional graphical models
• Bayesian isotonic density regression
• Hierarchical Dirichlet Processes
• Graphical Models, Exponential Families, and Variational Inference
• Markov Chains and Stochastic Stability
• Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering
• Bayesian Density Estimation and Inference Using Mixtures
• Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model
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