We propose a new scheme for selecting pool states for the embedded Hidden Markov Model (HMM) Markov Chain Monte Carlo (MCMC) method. This new scheme allows the embedded HMM method to be used for efficient sampling in state space models where the state can be high-dimensional. Previously, embedded HMM methods were only applied to models with a one-dimensional state space. We demonstrate that usi...

This paper proposes a new efficient merge-split sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit usingMarkov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs and Gibbs-like samplers for both conjugate and nonconjugate DPM models effectively update the model para...

A PM0.1 personal sampler devised by the authors (Furuuchi et al., 2010a) was investigated into its performance for the practical application, or on an improvement for a smaller cut-off size and also the removal of coarse particles using pre-cut impactors. The pressure drop through the inertial filter was discussed along with the separation performance since it is important to use a portable bat...

In this article, we propose a design method of selfinterference cancelers for wireless relay stations taking account of the baseband signal subspace. The problem is first formulated as a sampled-data H∞ control problem with a generalized sampler and a generalized hold, which can be reduced to a discretetime l-induced norm minimization problem. Taking account of the implementation of the general...

A new passive sampler based on low-density polyethylene (LDPE) layflat tube filled with chrome shavings from tannery waste residues was evaluated to determine volatile organic compounds (VOCs) in indoor and outdoor areas. VOCs were directly determined by head space-gas chromatography-mass spectrometry (HS-GC-MS) without any pretreatment of the sampler and avoiding the use of solvents. Limit of ...

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We introduce a stochastic version of the nonreversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear, to efficiently sample Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a smal...

This paper investigates the polar slice sampler, a particular type of the Markov chain Monte Carlo algorithm known as the slice sampler. This algorithm is shown to have convergence properties which under some circumstances are essentially independent of the dimension of the problem. For log-concave densities, the algorithm provably converges (from appropriate starting point) to within 0.01 of s...