A tutorial on Bayesian nonparametric models

DOI10.1016/j.jmp.2011.08.004zbMath1237.62062arXiv1106.2697OpenAlexW2115870554MaRDI QIDQ423105

Samuel J. Gershman, David M. Blei

Publication date: 18 May 2012

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1106.2697

zbMATH Keywords

Chinese restaurant processIndian buffet process

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Nonparametric inference (62G99)

Related Items

Topic model for graph mining based on hierarchical Dirichlet process, Bayesian testing for exogenous partition structures in stochastic block models, Inferring the number of attributes for the exploratory DINA model, Network structure exploration in networks with node attributes, Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models, Clustering and the efficient use of cognitive resources, A Bayesian nonparametric mixture model for studying universal patterns in color naming, Algebraic aspects of Bayesian modeling in psychology, Calibrating generative models: the probabilistic Chomsky-Schützenberger hierarchy, Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization, Jointly modeling and simultaneously discovering topics and clusters in text corpora using word vectors, Multiscale Modeling of Metal-Ceramic Spatially Tailored Materials via Gaussian Process Regression and Peridynamics, A Bayesian perspective of statistical machine learning for big data, The infinite Student's \(t\)-factor mixture analyzer for robust clustering and classification, Bayesian Estimation for the Markov-Modulated Diffusion Risk Model, A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions, Desirable properties of Bayesian models in perceptual psychology. A discussion of distribution families closed under multiplication, Network structure exploration via Bayesian nonparametric models, A Bayesian nonparametric model for multi-label learning, A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation, Bayesian nonparametric modeling and the ubiquitous Ewens sampling formula, A cognitive latent variable model for the simultaneous analysis of behavioral and personality data, A new Dirichlet process for mining dynamic patterns in functional data, Reliable clustering of Bernoulli mixture models, Geometric sensitivity measures for Bayesian nonparametric density estimation models, Cooperative hierarchical Dirichlet processes: superposition vs. maximization, Estimating the cognitive diagnosis \(Q\) matrix with expert knowledge: application to the fraction-subtraction dataset, A tutorial on Dirichlet process mixture modeling, Central limit theorems for an Indian buffet model with random weights, A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images

Uses Software

• BayesDA
• PRMLT

Cites Work

• Prior sensitivity in theory testing: an apologia for the Bayes factor
• How cognitive modeling can benefit from hierarchical Bayesian models
• Nonparametric Bayesian sparse factor models with application to gene expression modeling
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• An introduction to MCMC for machine learning
• An introduction to variational methods for graphical models
• Special issue on model selection
• Spike and slab variable selection: frequentist and Bayesian strategies
• Modeling individual differences using Dirichlet processes
• A Bayesian analysis of some nonparametric problems
• Bayesian Decision Analysis
• Hierarchical Dirichlet Processes
• Model Selection and Model Averaging
• Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach
• Generalized Spatial Dirichlet Process Models
• Bayesian Variable Selection in Linear Regression
• Hidden Markov Models With Stick-Breaking Priors
• Exact and approximate sum representations for the Dirichlet process
• Gibbs Sampling Methods for Stick-Breaking Priors
• Bayesian Density Estimation and Inference Using Mixtures
• Bayes Factors
• Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing
• Order-Based Dependent Dirichlet Processes
• Variational inference for Dirichlet process mixtures
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item