Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings samplers, are commonly used for sampling from multivariate probability distributions. A long-standing question regarding algorithms is whether a deterministic-scan (systematic-scan) sampler converges faster than its random-scan counterpart. We answer this when the samplers involve two components by establishing ...

Hamiltonian Monte Carlo and slice sampling are amongst the most widely used and studied classes of Markov Chain Monte Carlo samplers. We connect these two methods and present Hamiltonian slice sampling, which allows slice sampling to be carried out along Hamiltonian trajectories, or transformations thereof. Hamiltonian slice sampling clarifies a class of model priors that induce closed-form sli...

Passive sampling is an approved and accurate method for the assessment of organic compound exposure over long sampling time. This method could be very convenient for the short-time exposure assessment, but passive samplers have to be validated for this use. In this article, the behaviour of two commercial passive samplers (GABIE and Perkin Elmer) under fluctuant concentration conditions is stud...

We present sensitivity measurements of the EVLA at C (4–8GHz), X (8–12GHz), and Ku (12– 18GHz) bands to assess the feasibility of using the 3-bit samplers with these receiver bands, to quantify the resulting sensitivity, and compare it to the sensitivity obtained using the 8-bit samplers. In spite of the higher noise in data obtained through the 3-bit samplers, our on-thesky test results show t...

This paper proposes a new algorithm for Bayesian model determination in Gaussian graphical models under G-Wishart prior distributions. We first review recent development in sampling from G-Wishart distributions for given graphs, with a particular interest in the efficiency of the block Gibbs samplers and other competing methods. We generalize the maximum clique block Gibbs samplers to a class o...

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics. This insight enables extension of HMC and slice sampling to a broader family of samplers, called Monomial Gamma Samplers (MGS). We provide a theoretical analysis of the mixing performance of such samplers, proving that in the limit of...

We identify an open problem in game design assistance and automation: the development of reusable gameplay trace samplers. Inside many sophisticated content generators and design tools is a component that samples interesting and plausible sequences of player actions. Details and summary properties of these samples are used to assess generated content and to inform designers. As the development ...

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