Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Sprungk, Björn"'
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
In recent years, various interacting particle samplers have been developed to sample from complex target distributions, such as those found in Bayesian inverse problems. These samplers are motivated by the mean-field limit perspective and implemented
Externí odkaz:
http://arxiv.org/abs/2312.13889
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
Autor:
Rudolf, Daniel, Sprungk, Björn
Motivated by Bayesian inference with highly informative data we analyze the performance of random walk-like Metropolis-Hastings algorithms for approximate sampling of increasingly concentrating target distributions. We focus on Gaussian proposals whi
Externí odkaz:
http://arxiv.org/abs/2202.12127
For Bayesian learning, given likelihood function and Gaussian prior, the elliptical slice sampler, introduced by Murray, Adams and MacKay 2010, provides a tool for the construction of a Markov chain for approximate sampling of the underlying posterio
Externí odkaz:
http://arxiv.org/abs/2105.03308
Publikováno v:
Bernoulli 27(4):2267-2299, 2021
The linear conditional expectation (LCE) provides a best linear (or rather, affine) estimate of the conditional expectation and hence plays an important r\^ole in approximate Bayesian inference, especially the Bayes linear approach. This article esta
Externí odkaz:
http://arxiv.org/abs/2008.12070
Convergence of an adaptive collocation method for the stationary parametric diffusion equation with finite-dimensional affine coefficient is shown. The adaptive algorithm relies on a recently introduced residual-based reliable a posteriori error esti
Externí odkaz:
http://arxiv.org/abs/2008.07186
Doubly-intractable distributions appear naturally as posterior distributions in Bayesian inference frameworks whenever the likelihood contains a normalizing function $Z$. Having two such functions $Z$ and $\widetilde Z$ we provide estimates of the to
Externí odkaz:
http://arxiv.org/abs/2004.07310
When propagating uncertainty in the data of differential equations, the probability laws describing the uncertainty are typically themselves subject to uncertainty. We present a sensitivity analysis of uncertainty propagation for differential equatio
Externí odkaz:
http://arxiv.org/abs/2003.03129
Publikováno v:
In Journal of Non-Crystalline Solids 1 December 2023 621
