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MCMC for Imbalanced Categorical Data (Q5242484)

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scientific article; zbMATH DE number 7128448

Language	Label	Description	Also known as
English	MCMC for Imbalanced Categorical Data	scientific article; zbMATH DE number 7128448

Statements

scholarly article

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MCMC for Imbalanced Categorical Data (English)

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Journal of the American Statistical Association

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publication date

12 November 2019

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full work available at URL

https://arxiv.org/abs/1605.05798

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zbMATH Keywords

Bayesian

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data augmentation

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Gibbs sampling

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large sample

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Markov chain Monte Carlo

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mixing

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Bayesian Analysis of Binary and Polychotomous Response Data

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On the computational complexity of MCMC-based estimators in large samples

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The Pólya-gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic

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Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables

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Gibbs sampling, exponential families and orthogonal polynomials

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Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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An adaptive Metropolis algorithm

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Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions

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Bayesian auxiliary variable models for binary and multinomial regression

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On the Markov chain central limit theorem

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Bounds on the L 2 Spectrum for Markov Chains and Markov Processes: A Generalization of Cheeger's Inequality

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Computable bounds for geometric convergence rates of Markov chains

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Limitations of Markov chain Monte Carlo algorithms for Bayesian inference of phylogeny

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Bayesian Multivariate Logistic Regression

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Concentration inequalities for Markov chains by Marton couplings and spectral methods

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On the half-Cauchy prior for a global scale parameter

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Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

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Weak convergence and optimal scaling of random walk Metropolis algorithms

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Geometric ergodicity and hybrid Markov chains

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Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo

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Convergence Rates and Asymptotic Standard Errors for Markov Chain Monte Carlo Algorithms for Bayesian Probit Regression

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Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations

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The Calculation of Posterior Distributions by Data Augmentation

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On the computational complexity of high-dimensional Bayesian variable selection

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Identifiers

zbMATH Open document ID

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10.1080/01621459.2018.1505626

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:5242484

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