Markov chain Monte Carlo in practice
Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of
probability distributions commonly encountered in modern applications. For MCMC …
probability distributions commonly encountered in modern applications. For MCMC …
A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients
Abstract Background The Naive Bayes (NB) classifier is a powerful supervised algorithm
widely used in Machine Learning (ML). However, its effectiveness relies on a strict …
widely used in Machine Learning (ML). However, its effectiveness relies on a strict …
Analyzing Markov chain Monte Carlo output
Abstract Markov chain Monte Carlo (MCMC) is a sampling‐based method for estimating
features of probability distributions. MCMC methods produce a serially correlated, yet …
features of probability distributions. MCMC methods produce a serially correlated, yet …
On the limitations of single-step drift and minorization in Markov chain convergence analysis
Over the last three decades, there has been a considerable effort within the applied
probability community to develop techniques for bounding the convergence rates of general …
probability community to develop techniques for bounding the convergence rates of general …
Convergence rates of two-component MCMC samplers
Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings
samplers, are commonly used for sampling from multivariate probability distributions. A long …
samplers, are commonly used for sampling from multivariate probability distributions. A long …
Assessing and visualizing simultaneous simulation error
Monte Carlo experiments produce samples to estimate features such as means and
quantiles of a given distribution. However, simultaneous estimation of means and quantiles …
quantiles of a given distribution. However, simultaneous estimation of means and quantiles …
Exact convergence analysis for Metropolis–Hastings independence samplers in Wasserstein distances
Under mild assumptions, we show that the exact convergence rate in total variation is also
exact in weaker Wasserstein distances for the Metropolis–Hastings independence sampler …
exact in weaker Wasserstein distances for the Metropolis–Hastings independence sampler …
Lower Bounds on the Rate of Convergence for Accept-Reject-Based Markov Chains
To avoid poor empirical performance in Metropolis-Hastings and other accept-reject-based
algorithms practitioners often tune them by trial and error. Lower bounds on the …
algorithms practitioners often tune them by trial and error. Lower bounds on the …
Bayesian Estimation Approach for Linear Regression Models with Linear Inequality Restrictions
S Seifollahi, K Kamary, H Bevrani - arXiv preprint arXiv:2112.02950, 2021 - arxiv.org
Univariate and multivariate general linear regression models, subject to linear inequality
constraints, arise in many scientific applications. The linear inequality restrictions on model …
constraints, arise in many scientific applications. The linear inequality restrictions on model …
A Short Note on the Efficiency of Markov Chains for Bayesian Linear Regression Models with Heavy-Tailed Errors
Y Hamura - arXiv preprint arXiv:2410.17070, 2024 - arxiv.org
In this short note, we consider posterior simulation for a linear regression model when the
error distribution is given by a scale mixture of multivariate normals. We first show that the …
error distribution is given by a scale mixture of multivariate normals. We first show that the …