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Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/25
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data
Bayesian modeling case-control design data augmentation logistic regres-sion Markov Chain Monte Carlo population prevalence presence-only data simulation
2013/6/13
Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work w...
MCMC for non-linear state space models using ensembles of latent sequences
MCMC non-linear state space models ensembles latent sequences
2013/6/13
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no str...
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Sparse graphical model Reversible Markov chain Markov equivalence class
2013/4/27
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversible MCMC on Markov eq...
Fast MCMC sampling for Markov jump processes and extensions
Markov jump process uniformization MCMC Gibbs sampler Markov-modulated Poisson process continuous-time Bayesian network
2012/9/17
Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simu-lating from the posterior d...
Nonasymptotic bounds on the estimation error of MCMC algorithms
Mean square error Computable bounds
2011/7/6
We address the problem of upper bounding the mean square error of MCMC estimators. Our analysis is non-asymptotic.
Importance Re-sampling MCMC for Cross-Validation in Inverse Problems
Cross-validation Inverse Importance Re-sampling Model fit Re-use
2009/9/22
This paper presents a methodology for cross-validation in the context of Bayesian
modelling of situations we loosely refer to as iverse problems It is motivated by
an example from palaeoclimatology ...
Construction of weakly CUD sequences for MCMC sampling
completely uniformly distributed Gibbs sampler equidistribution probit quasi-Monte Carlo
2009/9/16
In Markov chain Monte Carlo (MCMC) sampling considerable thought goes into constructing random transitions. But those transitions are almost always driven by a simulated IID sequence. Recently it has ...
This paper surveys various results about Markov chains on general (non-countable) state spaces. It begins with an introduction to Markov chain Monte Carlo (MCMC) algorithms, which provide the motivati...
On the efficiency of adaptive MCMC algorithms
MCMC process asymptotic properties MCMC algorithms
2009/3/30
We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an ``optimal'' target process via a learning procedure. We show, under appropriate conditions, that the ...
A Mixture-Based Approach to Regional Adaptation for MCMC
Adaptive MCMC regional adaptation online EM mixture model
2010/3/19
Recent advances in adaptive Markov chain Monte Carlo (AMCMC) include
the need for regional adaptation in situations when the optimal transition kernel
is different across different regions of the sa...
Comparison of MCMC Methods for Estimating GARCH Models
Bayesian inference GARCH Gibbs sampler Markov chain Monte Carlo Metropolis-Hastings algorithm
2009/3/6
This paper reviews several MCMC methods for estimating the class of ARCH models, and compare performances of them. With respect to the mixing, efficiency and computational requirement of the MCMC, thi...
A Gibbs Sampling Alternative to Reversible Jump MCMC
Gibbs sampler Model switching Variable dimension
2010/3/18
This note presents a simple and elegant sampler which
could be used as an alternative to the reversible jump MCMC methodology.