Lithological tomography with the correlated pseudo-marginal method

Pseudo-Marginal Slice Sampling

Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the unnormalized probability distribution function to construct a Markov chain. However, the resulting chains are harder to tune to a target distribution than conventional MCMC, and the types of updates available are limi...

Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood

We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority of the existing methods for learning Gaussian graphical models are either re...

Pseudo-acoustic wavefield tomography with model constraints

Anisotropic wavefield tomography seeks to recover anisotropic parameters based on seismic wavefields. In terms of computational efficiency and degrees of freedom of the model parameters, the acoustic approximation to the anisotropic elastodynamics is more suitable than the full anisotropic elastic equations to the anisotropic tomography problem. We implement the wavefield tomography in VTI medi...

Marginal methods for correlated binary data with misclassified responses

Misclassification is a longstanding concern in medical research. Although there has been much research concerning error-prone covariates, relatively little work has been directed to problems with response variables subject to error. In this paper we focus on misclassification in clustered or longitudinal outcomes. We propose marginal analysis methods to handle binary responses which are subject...

Marginal Regression Models with VaryingCoeÆ ients for Correlated Ordinal