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Approximated Gaussian Random Field Under Different Parameterizations for MCMC
Fitting spatial models with a Gaussian random field as spatial random effect poses computational challenges for Markov Chain Monte Carlo (MCMC)...
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Bayesian Methods
Earlier, Bayes’ theorem was introduced. Now Bayesian methods are described for inference and information, especially using Markov Chain Monte Carlo...
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How to Improve MCMC Convergence
When modeling real-world data, MCMC may have poor convergence, which will make the calculation speed of sampling very slow. Poor convergence is...
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An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography
We revisit the Pseudo-Bayesian approach to the problem of estimating density matrix in quantum state tomography in this paper. Pseudo-Bayesian...
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Convergence Rates of Attractive-Repulsive MCMC Algorithms
We consider MCMC algorithms for certain particle systems which include both attractive and repulsive forces, making their convergence analysis...
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Particle rolling MCMC with double-block sampling
An efficient particle Markov chain Monte Carlo methodology is proposed for the rolling-window estimation of state space models. The particles are...
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A Comparison of Bayesian Approximation Methods for Analyzing Large Spatial Skewed Data
Commonly, environmental processes are observed across different locations, and observations present skewed distributions. Recent proposals for...
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Bayesian Methods
In this chapter, we detail one approach for drawing inferences based on the Bayesian framework for Hawkes processes, in particular using the Markov...
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A fresh Take on ‘Barker Dynamics’ for MCMC
We study a recently introduced gradient-based Markov chain Monte Carlo method based on ‘Barker dynamics’. We provide a full derivation of the method...
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Batch Size Selection for Variance Estimators in MCMC
We consider batch size selection for a general class of multivariate batch means variance estimators, which are computationally viable for...
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Bayesian Methods
In classical likelihood, an important goal is to learn about a parameter...
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Entropy-Based Subsampling Methods for Big Data
Under big data settings, parameter estimation can often become computationally infeasible for popular regression models. Subsampling techniques, in...
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Shrinkage Methods
Expression ( 6.51 ) indicates how prediction ability is governed by bias and variance. As models...
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Bernstein flows for flexible posteriors in variational Bayes
Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only...
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A Variational Bayes Approach to Factor Analysis
Factor analysis models are useful dimensionality-reduction techniques for the covariance of observed data. A Bayesian approach to inference for these...
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Plateau proposal distributions for adaptive component-wise multiple-try metropolis
Markov chain Monte Carlo (MCMC) methods are sampling methods that have become a commonly used tool in statistics, for example to perform Monte Carlo...
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Bayesian Estimation of Marshall Olkin Extended Inverse Weibull Distribution Using MCMC Approach
In this paper, we invoke a new prospective to discuss the estimation of a three-parameter Marshall Olkin extended inverse Weibull distribution based...
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An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models
Bayesian variable selection is an important method for discovering variables which are most useful for explaining the variation in a response. The...
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Sampling from the Posterior Distribution by MCMC
In the examples of MCMCMarkov Chain Monte Carlo sampling (MCMC) in the preceding chapter, no prior or likelihood was specified, nor was there any...