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We use the Monte-Carlo Markov Chain method to explore the dark energy property and the cosmic curvature by fitting two popular dark energy parameterizations to the observational data. The new 182 gold supernova Ia data and the ESSENCE data both give good constraint on the DE parameters and the cosmic curvature for the dark energy model $w_0+w_a z/(1+z)$. The cosmic curvature is found to be $|Omega_k|la 0.03$. For the dark energy model $w_0+w_a z/(1+z)^2$, the ESSENCE data gives better constraint on the cosmic curvature and we get $|Omega_k|leq 0.02$.
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimisi
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an ext
A novel class of non-reversible Markov chain Monte Carlo schemes relying on continuous-time piecewise-deterministic Markov Processes has recently emerged. In these algorithms, the state of the Markov process evolves according to a deterministic dynam
We propose a minimal generalization of the celebrated Markov-Chain Monte Carlo algorithm which allows for an arbitrary number of configurations to be visited at every Monte Carlo step. This is advantageous when a parallel computing machine is availab
Boson sampling is a promising candidate for quantum supremacy. It requires to sample from a complicated distribution, and is trusted to be intractable on classical computers. Among the various classical sampling methods, the Markov chain Monte Carlo