Zobrazeno 1 - 10
of 293
pro vyhledávání: '"Rudolf Daniel"'
This chapter surveys progress on three related topics in perturbations of Markov chains: the motivating question of when and how "perturbed" MCMC chains are developed, the theoretical problem of how perturbation theory can be used to analyze such cha
Externí odkaz:
http://arxiv.org/abs/2404.10251
Using the framework of weak Poincar{\'e} inequalities, we provide a general comparison between the Hybrid and Ideal Slice Sampling Markov chains in terms of their Dirichlet forms. In particular, under suitable assumptions Hybrid Slice Sampling will i
Externí odkaz:
http://arxiv.org/abs/2402.13678
We consider adaptive increasingly rare Markov chain Monte Carlo (AIR MCMC), which is an adaptive MCMC method, where the adaptation concerning the past happens less and less frequently over time. Under a contraction assumption for a Wasserstein-like f
Externí odkaz:
http://arxiv.org/abs/2402.12122
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:43571-43607, 2024
The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior. We explore the use of bijective affine tr
Externí odkaz:
http://arxiv.org/abs/2401.16567
We propose a theoretically justified and practically applicable slice sampling based Markov chain Monte Carlo (MCMC) method for approximate sampling from probability measures on Riemannian manifolds. The latter naturally arise as posterior distributi
Externí odkaz:
http://arxiv.org/abs/2312.00417
Autor:
Rudolf, Daniel, Schär, Philip
Publikováno v:
Statistics and Computing 34, article 20, 2024
Polar slice sampling, a Markov chain construction for approximate sampling, performs, under suitable assumptions on the target and initial distribution, provably independent of the state space dimension. We extend the aforementioned result of Roberts
Externí odkaz:
http://arxiv.org/abs/2305.03685
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:30204-30223, 2023
Polar slice sampling (Roberts & Rosenthal, 2002) is a Markov chain approach for approximate sampling of distributions that is difficult, if not impossible, to implement efficiently, but behaves provably well with respect to the dimension. By updating
Externí odkaz:
http://arxiv.org/abs/2302.03945
Probability measures on the sphere form an important class of statistical models and are used, for example, in modeling directional data or shapes. Due to their widespread use, but also as an algorithmic building block, efficient sampling of distribu
Externí odkaz:
http://arxiv.org/abs/2301.08056
We extend elliptical slice sampling, a Markov chain transition kernel suggested in Murray, Adams and MacKay 2010, to infinite-dimensional separable Hilbert spaces and discuss its well-definedness. We point to a regularity requirement, provide an alte
Externí odkaz:
http://arxiv.org/abs/2301.02426
For $\ell\colon \mathbb{R}^d \to [0,\infty)$ we consider the sequence of probability measures $\left(\mu_n\right)_{n \in \mathbb{N}}$, where $\mu_n$ is determined by a density that is proportional to $\exp(-n\ell)$. We allow for infinitely many globa
Externí odkaz:
http://arxiv.org/abs/2207.08551